OK. So, you're going to weigh too much of me starting now, and you're going to have even more of me, if that's not enough, on this website, speech.zone, that's the domain name, and on there you'll find a variety of things, and if you like to have a copy of slides in front of you while I'm speaking, you'll actually find them already on here in this special course at the top of the courses section. And later, when we do the hands-on, we'll use this website for the instructions for that. OK. So, if you want to follow along, there's a PDF of slides there, and we'll look at this site later on a little bit as well. Right. So, we've got two hours to talk about text processing, and there's a lot to say, and yet there isn't so much to say, because I think it's pretty fair to say that there's not been major advances in text processing. There have been very incremental, grindingly slow advances in text processing in the last 10 years, and a lot of people are very lazy about their text processing in their speech in the system. Certainly we are. So, people for English will typically use Festival, which is our system as their standard front-end. We haven't touched the text processing festival for a decade, and more than a decade, really. We built it. It was very good at this time, and it hasn't got any better. In industry, of course, things are very different, but the techniques haven't really advanced a whole lot. They were just kind of fixing the exceptions and writing ever larger dictionaries and rule sets and catching the edge cases, which is fine, and it works, but it doesn't scale. So, before we talk about all of this, let's talk about other places you can get information from. I would recommend two places. One, if you're not at all familiar with any sort of natural language processing at all, is just to start with this book here, and this is the excellent Juravsky and Martin. It's a thousand pages of excellent stuff, and for what it is, it's a very good value book, a big, thick book. An equally thick book, but a bit more expensive because it's a more specialised book, but even better is Paul Taylor's book with the excellent title, Text-to-Speech Synthesis, and it does what it says on the cover. It does everything in Text-to-Speech Synthesis, including Paul's attempt at signal processing as well, which is pretty good. And so these are the sources I would order to refer my students to to find out more about this. So we're going to look at in the next couple of hours, and we'll take a break halfway. What is this thing called a front end? And we're going to start a little bit philosophically by thinking, how does this problem of going from text-to-speech break itself down naturally into stages? If it does, and it may be useful eventually to think that it doesn't really break itself into discrete stages. But for the purpose of explanation, and it's certainly in the summer school, we're going to pretend that it breaks itself into these various discrete stages. And one of the stages, the first one, is to deal with this messy input text and get it into good shape in a way that we might then have some chance of knowing how to say it. So it's getting from how it's written to how it's said in the most general sense. And I think I'd like you to view text-to-speech throughout this and throughout all of the rest of the other presentations as this straightforward regression problem. A hard regression problem, but we can define it very straightforwardly as input text and output waveform. And how we get from one to the other is entirely up to us. We could imagine some incredibly sophisticated, powerful regression model that gets us all the way there in one giant step. And given enough data, we make it imagine that we might be able to do that and learn that relationship from arbitrary, raw text, unnormalised punctuation and abbreviations and everything else that's going on all the way to the waveform. Nobody can do that. That's just too hard. There's too many different things going on there to be able to pile all that into a model, into one model. And it's also very hard to get enough data to do that. So it does get broken down into stages and those stages are decided by experts where to draw the line along this long journey from text-to-speech. And just to be clear, what we mean when we say a regression problem, regression is the prediction of a continuously valued quantity in this case, the waveform. If we're predicting a discrete value of quantity, we call it classification. And later on, we're going to see in this front end, we're doing both classification and sometimes also regression. So let's just get a quick overview of what we need to do in this whole front end.  So we've also input text and we'd like to start discovering more about the text, things that are useful to know how to say the text. And some of them will be directly useful to say it. And some will just be needed by another stage further down the pipeline. So one thing we'd like to know about the text is once we've got words, they're parts of speech. And most importantly for spoken language, are they closed-class words? Are they words from a fixed list that we can't invent new words in for any language? Like the determiners of the and the articles? Or is it an open-class word where we can invent new words, content words, names of objects and things in the world? And we can invent new ones of those and they are more likely to get prosodic emphasis, for example. It's not directly going to tell us how to say it, but it might tell us later on what to do with prosody.  We might think that knowing something about the structure of the text might be useful. That might be helpful where to put phrasing. We certainly want to know what the pronunciation of this thing is, the sequence of sounds. In some languages that might be a trivial mapping from the spelling and in English it's certainly not. And we might want to start something sort of representation of prosody and so on, things like that. So that's what we're trying to do. We're trying to enrich the text with lots of information and we need to bring external knowledge sources to do that. And when we've got that we can then generate our waveform. And we're going to look at this in the other parts of the summer school later on, but we better just check we know what we're going to do so we know what the point of this front-end is. Let's just say that there are kind of two choices. One is we're going to concatenate bits of waveform and we'll find out this afternoon one way of doing that. That's called unit selection. Or we could build statistical models and they won't directly generate waveforms. We'll drive a vocoder of the sort we saw yesterday. And these look like discrete and very different ways of doing things. And it looks like there might be a very clear distinction between the stuff we do to text and the stuff we do here to generate the waveform. Hopefully in an hour and a half from now it'll be quite clear that that's not a clear distinction at all. And the responsibility of making some of the steps along this pipeline might rest with what we call the front-end or it might rest with what we call the regression model or the waveform generator. So this two-stage pipeline already is looking a bit dubious. This is how we might classically describe it. So let's call it a three-stage pipeline. We're going to go to this linguistic specification, all the classical text processing stuff, pronunciations, things like that, where phrases fall. We're then going to optionally or explicitly or implicitly depending on the type of system, we're going to get from that sort of what might call a linguistic structure to something that's a specification of the acoustics. It's not just how to say it but what it's going to sound like. That might be the input to a vocoder. It might be spectral envelope and F0, some acoustic specification. And then from there we'll actually generate the waveform. So what we found out yesterday was how to do this really, really well with very, very high quality, but what we didn't know yet was how to drive such a thing. And there's not a lot of point of having a very high quality model of a speech signal, whether it's sinusoidal or AQHNM or however many letters it's got in its name, if we can't drive it, if we can't predict its inputs well. And choosing a vocoder might be constrained by how well we can drive it. How convenient its inputs are, how convenient its representations are. So then we've got this journey from text to speech. So our input text goes in here and our output speech comes out here. And we've got this rather long and complicated journey to get there. And we're going to do that with a sequence of techniques and exactly how many there are in the pipeline and where their responsibilities fall actually varies a little bit with different systems. So let's just talk a little bit about different ways of getting through this journey. So it will become obvious what the front end is all about. And we're going to introduce some terminology here. It might or might not be what we'll hear in the unit selection session and I'll use it again later. In the classical unit selection system, we take the text and we predict a whole lot of symbolic things, the obvious one being the phonemes. We might predict symbolic representations of prosody, such as prominences and boundaries and so on. And then given only these symbolic things, we'll go and choose waveforms from a big inventory. And our choice will be based on matches or mismatches in these purely symbolic features. And in Paul Taylor's book he introduced some terminology to talk about this. It's some acronyms, we don't need to worry too much about them. But there's a way in which we only ever predict symbolic things in the front end and everything else happens when we select the waveform. Paul calls that the independent feature formulation because these symbolic features are treated as independently and we just have some weighted sum of them when we try and retrieve things in the acoustics. Some systems, in fact kind of all systems actually, even festivals that were claimed to only predict symbolic things, go one step further and predict some acoustic properties as well before going to choose the waveforms. Paul called that the acoustic space formulation. So you might imagine when we're going to do unit selection and going to retrieve bits of waveform from a database we want them to be of the right sort and that matching might be based on just their names, their symbols or it might be based on their acoustic properties. Are they the sort of duration we would like? Have they got the pitch that we would like? Or are we going to hope that the symbolic features will just get us that automatically? And then it's a very small step from there to go to what is now called a hybrid system and in the hybrid system we just go one step further and we just predict all the acoustic properties and in the simplest case we only use acoustic properties to retrieve waveforms in the database. So that's going even further down this pipeline. And going even further down the pipeline we don't just predict all the acoustic properties and then retrieve a waveform. We use the acoustic properties to then actually generate a waveform with another regression model, so another continuous prediction and that's statistical parametric speech synthesis using hidden Markov models or deep neural networks. And this is really, we could see this as something of a continuum. This is a slightly artificial categorisation and it's just a question of how we divide up this pipeline and how we assign the responsibility for getting us a little bit further down the journey to some module of the system. So in a classical unit selection system which is the one you're going to hear next we've got a front end just like I'm about to describe it in the next hour or so. We have a very implicit regression model. We may or may not explicitly ever predict any acoustic properties. The regression might be implicit in retrieving units from the database. By retrieving a unit we get a continuous valued thing, a waveform, and that's a prediction. It's a very implicit, funny sort of regression model but it is a regression model and the waveform generator is almost trivial signal processing. It's concatenation of pre-recorded waveforms. Like all trivial signal processing it's really hard to implement it well. The theory is very simple. The implementation is all about details. So that's what the picture you should have in mind as we're talking through the front end and you're thinking does this belong in the front end? Is this part of the regression model? Is this waveform generation? Don't worry too much what you call it. We just stepped along the journey. So we're just going to incrementally add information to the text. Some of the information might be symbolic. Eventually some of the information might be continuous. So durations, we might predict durations of things. And then how we eventually generate the waveform might be to retrieve things or it might be to get acoustic specification. So how we divide this journey into little steps is entirely up to us. It's a design choice. There's no right and wrong answers. Some just work better than others and that's an empirical question, not a theoretical question. So let's see in pictures then the sort of things that typically happen in the front end and we'll just dive into detail in a few of them. I'm not necessarily going to use all the slides if you've downloaded them. We'll pick and choose from those slides to see how the time goes. So inside the front end it should be unsurprising then that the typical architecture of a front end is what we call a pipeline. It's a very naive architecture and it's just a concatenation, a sequence of processes, many of which require the output of the previous process to do their job. So we need to predict part of speech before we can look at words in the lexicon and we'll see why that is. And we need to predict certain things before other things in general. Now that's kind of linguistically obvious that there's these hierarchies of linguistic information, some things depend on others. From an engineering point of view this is a terrible architecture because we get a cascade of errors. None of these things are perfect and any error we make early on in the modules. So this tokenisation, if we break the text into the wrong parts, if we cut something that should have been one word into two parts, it's very unlikely that further down the pipeline anything will think to glue them back together and recover from that error. So we'll make lots of unrecoverable errors. Part of speech tag errors. If we tag something as a now when it should have been a verb and we go and look it up in the dictionary and there happens to be a different in pronunciation, we're probably not going to recover from that. So we're making hard decisions all the way down. So that's a weakness of this kind of architecture. So you can immediately start thinking there might be better architectures than this. Or what festival does and I'm going to guess this is what a lot of commercial systems do as well. The other weakness of this architecture is that each of those steps is expensive to make. It's expensive in human effort and it's expensive in knowledge required and expensive in data required. We need to know a lot and we need to be expert to build all of those modules. We might even need to be very expert even to annotate the data to do the machine learning if we're using machine learning. Sometimes we can get naive people to label things but very often we need experts. Labeling prosody is something you need to learn how to do. You need to be trained how to do. So that makes it expensive and now you understand why even nuances language catalogue is our 40 languages and Google say they're so ambitious we've got a huge target, we're going to do 200 languages. That's 200 out of 6000. And the rest of those will be very, very expensive to do because we don't know much about the language, we don't have a lot of data, we might be able to find someone who's linguistically expert. So to summarise, the architecture is a chain of processes each one could be rather expensive to make in various ways in time, in money, in data, in expertise. So let's look at a few of those to see why that is, to see what sort of data they need and what sort of knowledge you need before you can make any of these modules. Imagine you'd like to make a system for new language, what would you have to go and do?  Don't be afraid, we're all happy with this concept. So it's going to affect our look-up in the lexicon so we get a matching part of speech for homographs spelled the same, different part of speech, different pronunciation. This is well-established technology. It's a pretty much a solved problem in NLP if you've got the data. And the data are simply large amounts of text that have been tagged. So that's boring and you need a lot, millions of words to make a really good part of speech tagger. So we need to annotate actual text with part of speech. The good news is we don't need it's read out loud. We just need text. The bad news is that when we do that we tend to choose text that's completely inappropriate. We'll just pick newspaper text because it's convenient.  So we'll always already have a mismatch. Nevertheless, these part of speech taggers are pretty accurate and are not really a limiting factor. Another process we might find further down the line is to go and find pronunciations of words. An obvious way to do that is to look up in a pronunciation dictionary. We'll always find that there are words not in the dictionary and we have to go and do letter to sound for those. So we'll see a bit later on that there are classical methods for converting letter to sound. We'll look at a really simple one and I'll explain why we're doing the simple one later on. How would you write a dictionary? A lexicographer needs to be a native speaker, needs to be linguistically sophisticated, needs to choose the set of symbols for pronunciations, needs to choose the phonemes, which might be non-obvious, and then just needs to sit down and write a dictionary. That's kind of tedious and we might write 20,000 entries and then we go and synthesize and find that there's a word that we forgot and there'll always be a word we forgot because it was a new word. So we need to extrapolate from that dictionary. We need to learn what's often called letter to sound rules. They're not really rules. Think of it as a statistical model. It's a classifier predicting symbols. The training data for that classifier would just be the pronunciation dictionary. Now languages where we don't need a dictionary, we do have to literally sit down and write rules and for languages like Spanish, we can probably do pretty well with rules. Fairly comprehensive. Until we get a loan word or an exception and then we'll have to go and write a dictionary anyway. So how do we train letter to sound? We take all the words in our dictionary. Some correspondence between the letters and the sounds. Some sort of alignment. That might be sort of slightly expert driven. So a somewhat ad hoc alignment. So reformat our data. So it looks like nice, neat machine learning data. And then we'll learn a sequence to sequence model that learns to go from the sequence of letters. So it might learn to map from this letter to this sound and in doing so it'll look at the context in which this letter occurred. And so we'll build a classifier whose inputs are that and output is that. The nuance's catalogue and Google's aspirations for a catalogue wouldn't include rules as such, just the annotations. For the letter to sound? Well you mentioned that nuance has this catalogue or other languages that it doesn't include letter to sound rules. So the question is do all the languages need letter to sound rules? No, I'm just wondering what the catalogue sounds like. So in a big catalogue of many languages that some company has constructed or some researcher has constructed each and every language would involve building all of these modules. Which is why this catalogue is rather limited in size. So we'd need to sit down and either write the rules by hand or create the dictionary and then use machine learning to extrapolate from the dictionary. I would say that every commercial system does something for letter to sound. You may occasionally be able to treat the letters as if they were sounds and just do a one to one mapping. So a trivial letter to sound model. When I say catalogue, I meant the list of languages they have available for sale. So actual working systems. Let's move on in this pipeline. It gets worse. Some of these modules in the pipeline as we get further down are not just doing these classifications where we can craft the data. They're doing some regression onto acoustic properties and therefore we're going to need speech to learn them. If we're doing classical supervised machine learning which is the one thing that works best we need some annotated speech to learn some steps. So an example of that might be to predict where to put phrase breaks. We should not just signal by pauses. They're signal by drops in F0 and resets and duration effects around the boundary. So here's how it's done in festival. You take some speech which fortunately you find someone else is annotated but isn't very large and that will be annotated with the thing we're trying to predict prosodic phrase breaks. I still don't have your playback. So imagine someone saying this sentence. A 1918 annotator will annotate there was prosodic phrase break there. So the output of such a process will be a big data set probably not that big. You'd be lucky if it's an hour or two of speech and it's annotated with where the breaks were and then we'll use another sequence to sequence model to predict breaks at run time at synthesis time. So this sequence and typically we won't predict from the words we'll predict from part of speech because words are too sparse and there'll be unseen items we'll collapse them to parts of speech to categories and learn a sequence model and you can see that for every sentence we might only have one positive example of a break. So we need even from an hour of speech let's imagine 500 or 1000 sentences and we're not going to have a whole lot of positive examples of phrase breaks to learn this model. So this is tough for machine learning. This is very sparse data. That was a little whistled stop tour of the sorts of things that happened. Let's go into a bit of detail in some of those. Not all of them. So the question is do we annotate more than phrase breaks? Yes, we'll see in a minute. In quite a few minutes we'll see that we could choose to annotate Toby Symbol's full prosodic annotation if we wish to do so. We would need to manually annotate the training data in order to train the classifier to do that and indeed that is what the festival does. Just one thing to say since we make the recordings it is nice also for the others who are not here to hear also not the answer but also the question. I'll try to repeat the question. No, no, it's not this to say you just have to press the speak and then here and then... Okay, let's try that. I can't repeat the question anyway. It's ingrained. I'm going to take the festival speech synthesis system as a canonical boring traditional conventional example of a text-to-speech system. It's the one you're going to use in the lab. I couldn't possibly say it's state-of-the-art. I've already said that the front end hasn't been touched for a long time but I think it's still pretty typical of what is done and if you wanted a commercial system that was like this you just add more rules more exceptions, longer dictionaries longer lists of acronyms and clean up all of those edge cases. Maybe use smarter machine learning models better fancy a part-of-speech tagger and so on but the concept is going to be very similar. So that's the list of things that festival does and let's start going down the pipeline and see how far we get before the first break. So the first thing that Paul Taylor calls in his book Text Decoding is actually to find the words because text isn't made of words text is made of characters and so we might have characters like this one here's a sentence it's not made of words it's made of some things that are very obviously words that's a word I'm assuming this is a word but it might not be in my dictionary so I'd have to check that that's not something else that's not a word and that's not a word What do I mean by a word? I mean something we might plausibly find in a pronunciation dictionary imagine a really big pronunciation dictionary one of those 20 volume ones on the shelves that someone's listed everywhere that we can think of if we think we might be able to just directly look it up in there then we can say it's a word this is not going to be even in the Oxford English dictionary it will be ridiculous to start listing all possible numbers so we're going to have to turn things into words so we need to find the words and before we can even do that we need to break this thing into chunks that we can process further and that's called tokenisation tokenisation is typically going to be done with some handcrafted things, something like a regular expression or some rules it's going to depend a lot on the language so in English white space is a pretty good clue it's not the only clue because we can see pronunciation next to a word without white space so we need to know what counts as pronunciation and what's not pronunciation that's language specific punctuation symbols in Spanish that don't occur in English upside down question marks if that's not in our list of punctuation it will get treated like a character and things will go wrong and this is pretty straightforward so we could do a decent job of that once we've chopped it into tokens we're then going to go through each token one at a time and decide if it's a word or not and if it's not a word do something about it turn it into words so we need to detect whether things are words or not the classical way of doing that will be to write long lists of all the things that we know about all the acronyms, all the abbreviations and so on and just have a big look up table that helps us expand all the ones we've already seen before for the ones we haven't seen before we might do something like regular expressions so to detect numbers we can easily write a pattern that is a sequence of digits the key problem though in doing that is that these things are ambiguous so although you might have detected that this needs some further processing you might also have detected that this needs some further processing what you then do to it, what it expands to depends on the context in which you find it so it's not, we can't just do everything with a look up table because we wouldn't know whether that's doctor or drive and likewise numbers we could sit down and write by hand lots of expressions that match currency amounts or year amounts, dates and so on and this is kind of what festival does so something appears in a format that we didn't think of when we wrote those rules it gets skipped by the rules and get handled by some default rule that maybe just read the digits out as individual words, as a fallback so it's hard to say anything theoretical about all of that it's a bit ad hoc that's because language is messy, there's no reason to think that there will be some beautiful theory of text processing text is an evolved way of communicating it doesn't necessarily obey rules people are creative and productive they invent new things so there's no reason to think we should be able to write one elegant model that does all of text processing it's perfectly reasonable that it will be a sequence of kind of messy things we can say something about a general principle though of the sort of way that we might go around processing the text and that we might break it down into some stages we might first detect that things are not standard words might look them up in the dictionary if we don't find them we'll say it's not a standard word that might be a very simple way of detecting it once we've done that we might say what sort of non-standard thing is it and we might have a set of categories for that years, currency amounts dates, times and then some catch-all general rule that just says read it out as a sequence of words the best you can and then for each of those separate classes some way of expanding that so there's very specific ways of expanding if this looks like it's going to be a year there's very specific rules about how to say that in each language we can just write rules if it was a case where rules will work fine we don't need machine learning for that so exactly how we detect how we classify, how we expand is up to us there's lots of classical and simple methods for doing that detection might be some simple rules classification might be a very simple machine learning classifiers such as a decision tree an expansion could be lookup tables or transducers or anything else you like typically these will be very simple forms of machine learning because more sophisticated forms of machine learning we might be tempted to throw a neural net at everything we might find that we just don't have enough data to get good performance out of these fancy machine learning methods they're not as efficient with the data we might find that a regular expression a transducer, a handcrafted decision tree actually is more efficient with the data that we've got it makes better use of the very small amount of data so we've spelled everything out as words it's going to be somewhat language specific it's going to be tedious to build and we're not going to get many papers out of doing that it's got to do it and then we've got to start our journey getting towards how to say those words some of the things now that we could either look up in a dictionary and if we don't find them we can convert the spelling into a pronunciation some other way and then we can start saying them start adding more acoustical information so let's talk about some of the things some of the steps that might happen along that journey and some of them are much more helpful in some languages and others I'm not going to say anything about morphology morphology will be to break these words down into the smaller constituent parts in the hope that the pronunciation of a big complicated thing is made by the pronunciations of it smaller things concatenated possibly with some context dependent rules it doesn't help very much in English on the analysis side so given an arbitrary word that's not in our dictionary to automatically morphologically decompose it is error prone and is not necessarily going to be super helpful in some other language like Finnish where we've got some impossibly large possible number of word forms it might be essential to try and break it down but morphology is useful in English in actually creating the dictionary in the first place so the lexicographer when he or she wrote those 20,000 words might not have wrote them all by hand might have written a lot of base forms and some simple rules and said let's just make lots of plaurals by adding s wherever it's possible or es when necessary so we could generate forms in the lexicon by rule that's where it's going to work decomposing unseen words is going to be much harder we've already talked a little bit about part of speech tagging I'm not going to say so much more about that let's go straight on to looking things up in the dictionary and then worrying about what to do and we don't find them in the dictionary so pronunciation of words what to do about turning this string of letters into a string of pronunciation symbols phonemes typically so a language like English it's important to have a dictionary here's some annoying misuse of terminology lexicon they say dictionary they use them interchangeably I don't know what the difference is anymore dictionary will be more correct because it says how to say things lexicon's more just a list of words nevertheless you'll find both used in the literature a dictionary for text of speech needs to be a bit more sophisticated than one for speech recognition in speech recognition all we would care about would be spellings and pronunciations and we just use this as part of the creative model of speech recognition to generate from words to subword units in synthesis we need a little bit more than that because we're not going to use it in that way we're going to have spellings and we're going to look up the phonemes and it's more important that we get the right phonemes here so if we've got two identical spellings we're going to have to make a hard choice between these two things we can't make some probabilistic choice and say let's try both let's try saying it as both and seeing what happens speeches and like that there are hard decisions we need to pick one and stick to it we can't say something halfway between these pronunciations and we can't say some distribution over them we're going to pick one and so we need to make a choice between these two homographs and in this case it's easy because one's a noun form and one's a verb form so we've got lives the plural of life and lives to live so now we know why part of speech tagging is important to go and look up the right thing in the dictionary but the case is that this doesn't work for there'll be words that have the same spelling and the same part of speech and still have different pronunciations there's examples of this in the textbooks lots and lots of examples of words like that the favourite one in all the textbooks so we have to use it as this word here it's a kind of silly one because it's quite a low frequency words it's either bass which is a musical term or bass which is a fish I've never had to synthesise bass the word fish except when testing festival but nevertheless they're both nouns and we couldn't discriminate between them in the part of speech tagger so we need to do something called word sense disambiguation and we're essentially just going to look at the words in the context and if in the rest of this sentence there were some music sort of words that were co-located we'll pick the musical sense if there were some fishing sort of words then we'd pick the fishy sense and this is a standard natural language processing problem word sense disambiguation and we can do it very straightforwardly and fairly accurately so looking things up in the dictionary is fine if we get the part of speech tag right we might need to do this clever word sense disambiguation first and the lexicon would have to have both senses and have some extra annotation saying this is the fishy sort of B-A-S-S and this is the musical sort an extra tag so when we look it up from our word sense disambiguator nevertheless that's a completely solvable straightforward natural language processing problem couldn't it be hard for unseen words someone invents a new word and then uses the same spelling for two new invented words we'll have a very hard time discriminating between those two but we need more of them this string of pronunciation symbols we need also some structure within the word structure of the sounds not structure of the spelling not the morphology but the syllable structure of the sound and so this is going to be absolutely essential when we come to start deciding how to pronounce this and that's going to be true whether we're doing unit selection waveform concatenation or a statistical model in all of those cases the structure of the syllables will be very useful and either choosing appropriate units and where to make joins is predictive features for our regression on to the acoustic promise of a vocoder there are various reasons for that the most obvious one is that syllable structure helps us decide where to put prominences where to put prosodic events so in the dictionary itself we might already start assigning some prosodic features so there's some prosodic features that belong to words themselves and they tell the difference between two words in tone languages and the other pitch features called tones we could also mark up in the dictionary we'll discriminate between words so we've got two words here and they've got the same spelling on a different part of speech but not only is the phoneme sequence different the pattern of emphasis is different so we'll call this lexical stress lexical because we can mark it in the lexicon and it's specific to the particular word and in festival everything's marked up in this horrible bracketed notation that's just the language it uses and we mark stress with ones and lack of stress with zeros so we might have present and present as these different word forms and getting that wrong is going to be a very salient area to the listener it's a different word so you've got a different meaning I'm a little bit confused between the one and zero syllable stress so the question is whether it's one and zero syllable stress I'm going to call this lexical stress and it's a feature that attaches to the syllables of a word and in every word there's going to be one syllable that's got the primary lexical stress and there might be another syllable that's got some secondary stress in a long polysyllabic word you might even imagine some sort of tertiary stress in a sufficiently long word there might be a third place that gets a little bit more prominent as well but the key is to get this primary lexical stress and we can mark it in the lexicon but it doesn't mean that we'll get some enormous pitch movement at Synthesis time it means it's the sort of place we might do something with prosody so what does lexical stress do for us it says when we're coming later to predict prosody this syllable is a very likely candidate to do something interesting with to make it longer, louder to move the pitch around and this syllable is a very unlikely place to do something you probably don't want to go when making this syllable sound longer and louder and have some pitch movement you might want to do exactly the opposite you might want to make it shorter, quieter and even make the acoustics somewhat reduced already this has got a reduced vowel in it phrasal stress how does this work with the phrasal stress so the syllable stress or the lexical stress is different from the phrasal stress so let's think of lexical stress as possible locations where something prosodycally interesting might happen but it's not guaranteed to happen, we don't know yet we're only talking about words in the dictionary in isolation in their canonical form things are going to happen to them when we start putting them in context for example if this is the focus of the sentence or not and what other words around it are going to have prominences or saliences so think of the candidate sites ready to receive some prosodyc event they may or may not get one we're going to have to compete with the other words to see who gets them because in any one phrase or any one sentence we can only have so many prominent or salient syllables if we put pitch accents on all possible lexical stress syllables the speech will sound hyper articulated and a bit over excited and unnatural so we need to some of them won't get these events or will get smaller events, we'll come to that so how do you actually like split a word into a bunch of syllables and does there actually like exist multiple ways of splitting like in the case of the first present it's like do it like p, r, e, h and z, e, x and t yeah, excellent question so question is how do we syllabify the words first question in the lexical we do it by hand because we have knowledge we are an expert we have linguistic knowledge of the language so we write down the correct salarification for unknown words words that we're predicting with letter to sound that we'll see shortly we also have to predict the salarification so we need some statistical predictive model of salarification can we use rules also this model of salarification could be a fancy statistical model it could be just some rules and indeed the words in the dictionary we also might have made some explicit or implicit choices when we manually syllabify those as to as the question implied there might be some consonants here it's not entirely obvious which syllable they might belong to is this present or present both of those seem plausible and reasonable to me as a native speaker so there are various principles and rule-based systems on which you might do that one of which is called maximal onset she says put as much on the beginning of syllables as possible but there are other schemes as well so this is for English and I guess it's maybe in part following linguistic tradition to think about stress accent as other being primary secondary or off when you have a Chinese lexicon I'm assuming that you need lexical tone in here as well and then you probably again follow the traditional grammarians by putting one of four possible tones and I'm wondering whether there are any kind of anecdotes of experiences in TTS where the linguistic background story about stress or tone or whatever it is that goes in this lexicon has proven to be simply insufficient and you need something else to support inference of the kind that all of these people are talking about in order to actually find the trajectory and know the duration and all of that or do people just generally follow the linguists so in terms of what you manually mark up in the lexicon when you construct it I suspect people stick to well established knowledge in conventions and I think you're implying that the knowledge that's present here in the dictionary may or may not be acoustically accurate and so that's a fair question and I think in statistical parametric systems we do have some chance of recovering from that if we systematically see disagreements between these annotations and the way our speaker spoke and there's enough data and we can somehow predict from context what the difference between those two is then we can still get the correct pattern of stress the same probably applies to tone that's problematic because there's simply probably not enough data in our speech database to find those sort of systematic discrepancies between what we marked up in the dictionary what we get in the speaker's rendition of the text I don't have a lot more to say about that other than it's one of these things people haven't looked at a lot recently and don't tend to evaluate in these terms to detect if this is a particular problem I'm going to hypothesise this is not a particular problem because the intelligibility of current systems is extremely good and errors here would affect intelligibility on some extent as we get towards the first break let's just start talking about going from letter to sound now for some languages we'll always use Spanish as the canonical example of this phonetically beautiful five vowels very neat and tidy you think it's not quite so neat and tidy the relationship between spelling and sound is very regular as my wife who actually teaches Spanish says you say every letter except you don't say H except in some cases except in some parts of Spain you go to Seville they just chop the ends of things off and so on so it looks neat and tidy in its canonical perfect form but it's not so with every speaker and in that relatively simple case we could actually sit and write rules by hand of this sort of form so let's do it for English because that's the language we have in common we start to sit down and we'd like to write some rules for the letter C C appears in lots of different contexts and has lots of different pronunciations it can be CACHA or it can just be silent it just depends and we could start thinking right if it's at the beginning of a word there's nothing before it and it has an I after it after what happens next this will be SA where it's like system so we can start writing these rules down and this works very well if things are regular and it looks like it's going to start working well for English but it won't because we'll need a very large set of rules and as soon as we have a large set of rules we'll get inconsistencies between the rules we get conflicts between rules so rule-based approaches are great if the set of rules is small the order in which you apply the rules is obvious if the order in which you apply them is not obvious if changing the order changes the outcome and there are a nasty interaction some rules need to know the decision of a previous role then before we know it we've got some horrifically big bowl of spaghetti and these rules become impossible to maintain and fixing an error in one place creates two errors in another place and so for that reason we'll tend not to sit down and write these rules for English and probably some other languages where that applies too and I was going to finish off saying one little thing about letter to sound what we do after we look things up and then we're going to just rewind and go and look at one very simple form of machine learning which you might be a little bit familiar with but it's so important that we're going to just go over it in a bit more detail and that's classification and regression trees first of all we use this classification regression trees for everything it's the neural net of the early 1990s if we don't know what to do we throw a classification or regression tree at it and it works great up to a point if there's data let's just say what happens after we've done that just to wrap up that part pronunciation can't be entirely determined on a word by word basis there are things that happen when we put words in sequence so we can only do certain things after we've looked up in the lexicon or the dictionary and so they're called post lexical effects in the vast majority of systems all systems I'm familiar with the number of post lexical effects that we attempt to deal with is very very small and we just pick the most important ones so in French it might be liaison in English some accents in English it might be this linking R we need to make an R sound to link two words together it might be devoising things at the ends of words in certain contexts and so on so a very very small number of rules and in festival this set is small and some of it is just hardwired into the code because they're so important and so predictive so useful that they're just always there and they're always on some might be specific to the speaker in which we might need to turn them on or off depending on the accent of the language that we're dealing with this will be completely language specific all languages do things to words as we string them together no language sounds like isolated words concatenated right we just we're going to watch 5 minute video and then take a break and then we're going to come back and we're going to talk about the video so you can give some time to think about it we do need audio we do yes we do I'm not getting myself then what will day we'll take a break and then after the break we'll watch a video with sound that's a good idea ok sure thank you very much


Bro Retirement oh, I feel so bad big bad hall of fame quarterback But he retired like a day before Tom Brady technically supposedly retired and I feel like we all forgot about it I feel so bad big bad just got this crack Card in Madden 22. I can't wait to get gameplay and show you guys this but I feel like we've been having to make too many tribute videos I just had to do a tribute to Gabriel Davis who had an insane playoff game But it's gonna get swept under the rug cuz the only thing we're gonna talk about from this season is Joe Shiesty or potentially Matt Stafford huge win-win for Papa Meeks there. Wow. Do I feel bad for Big Ben? He had like a day. There was only like a whole day where the media cycle was talking about Big Ben So I do feel bad Madden didn't forget and I'm not forgetting either He got a 98 overall feel general better off this burger with insane stats and 82 speed 65 240 89 strength that quarterback 97 throw power insane accuracies. He's amazing.  He's got a scape artist, right? Hot route master additional hot routes on anybody any position and protect its stronger past blocking already Protected with escape artists is so stupidly good You get better past blocking and you can scramble at any time and then how about master I can give any routes anybody that we had to go with brick wall We'll probably never activate it cuz we're playing the college overtime house rules brick wall is very Big Ben Playstyle I understand that escape artists is not exactly like prime Big Ben playstyle.   I guess he will get legends cards in the future I don't know if I'll ever use them though. Also, by the way, I'm that whole Brady thing He didn't technically retire though, right? So not only did Big Ben get swept under the rug for his retirement, but it's it's not even a real Tom Brady Retirement so that like almost just sucks even more I didn't activate it. I have Tebow activated. Oh There it is, baby. I still have Tebow with an ability on him. We're gonna run this guy. Let's go Let's go going up against young Herbert and the Colts. Oh boy. By the way, this is college overtime rules Hopefully you guys know those if you don't you'll learn Stop I accidentally shouldn't use her my D. Lyman, but it actually kind of worked I'm a sense of heater is in the Ray Lewis a lock up the middle here Y'all let's go dude I don't even think I'm gonna get an offensive turn here because as soon as he doesn't convert this He's gonna quit I think 4th and 25 he goes play action. There is literally nothing open Let's see if he quits because I really want to get Big Ben out there. I don't think I'm gonna get to We got some threats. We got Odell DK Metcalf Cooper Cup Darren Waller and Adrian Peterson. What about Waller? It's just so a laser to start it out. It's he said a brand new animation Never in my like six or seven straight months of playing this game. Have I seen that animation?  I'd be willing to bet I could just whip around.  I just had two of my luckiest plays of the year in succession like back to back It's the Big Ben effect, baby. Let's go also of course a huge thank you to Vessie for sponsoring today's video If you have not heard of this brand listen up. They make some incredible shoes. So this right here is my newest Vessie shoe It's the men's everyday move in polar white shoes already fired I can't wait to wear these but on top of that these shoes are completely waterproof and snowproof and that's not an Exaggeration nothing gets through a Vessie shoe, which is why they're the perfect everyday shoe if I'm walking Luna I can promise you I'm wearing a pair of Vessie's because no matter what I encounter the Vessie's are gonna be able to handle it They're comfortable lightweight and breathable, which I love as well and the material they use is called Dima Tex It's a dual climate knit material So in the summer your feet stay cool But in the winter your feet stay warm and they're sustainably made which is always a plus less water waste no animal Byproducts and a hundred percent vegan shoe I have to show off the waterproof feature you guys have seen me do this a few times because I've been working with Vessie for So long but guess what my phone has never gotten any water damage because these shoes are invincible I'm gonna go dunk my Vessie's and a bunch of water with my phone in it And I'm incredibly confident that my phone's gonna be unscathed. Let's go check it out. Oh, he's in there I'm over. Are you tired father? No, babe. All right y'all ready for this? Moment of truth, baby. Look at that Perfectly dry foam. Yeah, they're awesome. Like I said Vessie's are my go-to shoes at the front door check them out at the link Below it's Vessie.com for your pair of Vessie shoes Vessie. Thank you for sponsoring guys. Enjoy the rest of the video Justin Herbert again. All right first to go from the five run run. No run play. Let's get a pick He wants to throw right back across. Let's go back. Oh I could have sworn he's gonna throw back across but he doesn't do it smart decision I got to cover a lot here on second and goal because we're sending the blitz but screw it Let's do it. I got the left side All right, I'll play does he run it? No, it's a pass right side's not open back across as it opens. No way No way. We just got to beat Mike Evans. Wait. Hit you back. We have nothing to lose. We have literally nothing to lose 60-0. Let's go. We could score kick up P.A.T game is over. Damn. I was hoping to get the two points Really, it doesn't matter in that scenario. See who he's gonna use or Ray Lewis He's not gonna be able to get the waller and we don't fumble So we're in a good position Rathaus burger with a rifle. I want to scramble in the Rathaus burger And in fact if anybody gets to my way, I'm just gonna truck straight through them.  I Was look I was looking for the truck. I mean I was gonna get all boys and I Hope I can erase the vision of Big Ben hitting that from my memory. That would be really nice. Oh He said what a guy he said gg bro big fan keep up the great work.   All right, I'm not gonna lie.  Let's go, baby. Yes, sir The two grittiest guys on the team getting it done on that drive. Oh back Roughing the pastor just saved me I was about to say cuz that honest to God was not a very good pass. It really wasn't Protected escape artists. I'm telling y'all it is such a broken broken combo Oh Misses a pass there. The reason it's so good is usually when you start to take off as a scrambling quarterback Those d-line and will instantly shed you and just go get you and it's like it's no fun to scramble this year No, this is fine. He can't get that fourth in one. We just got to play under I think you want something short here will go QB spies in the middle He runs it. I'm fucked. If he runs it. I am fucked. No run. We're good right here Let's go diving swap from Jaylin Ramsey. You love to see it second and 11. Oh boy.  It was also dog water though. So Nothing's open He'll probably quit I would have to guess I'm just gonna like predetermine this Metcalf route Can he get there can he get there? Oh my god, big man had a guy right on him.  I think face clay might be dropping after that one.  Ooh that rope from cup was nasty.  What's our money play to get in here? Oh, that's hot. Wait a second.  I don't know why I threw that second and nine Screamers coming left boom Huge cuz if he scores I lose these scores at all I lose so it's five wide But it's four loaded to the right side. You do not see this very often very obviously a pass So I got two underneath here. Oh What? So he does hand it off going absolutely no, I thought that might have been a read option I'm on one step. That's not who I want to be on not how I want to be on okay? That actually works out just fine. We're gonna let the defense carry this game. Okay. I'm sorry It's supposed to be a big bad trivia video. I mean Fourth and 12 one more stop. We got this fourth and 12 Okay, so you actually didn't I thought he caught that sorry Harbaugh not today, buddy Oh, there's a Ravens team team. I just noticed at the last minute Ray Lewis Lamar Jackson Harbaugh We do not care Golden guy. What do you got golden guy? Jefferson Henry, okay should be a W walking W right here for big man scramble Tom Brady makes a laser laser of a throw Well, it's so let's do some rethinking on the defense here. That is the scramble Tom Brady, right? Yeah, it is This is the scrambling Tom Brady. Oh Left side Derek Henry. Oh, I go verly. God. It is hard to stand that Derek Henry up second and goal Come on, baby. Shorty get in there. Ah, somebody get in there. Sheesh. Oh No, right side Yes, just barely got he's so hard to take down dude. That's Ray Lewis right there Ray Lewis hit him first Still bounced off it, but Devon light cleaned it up. I'll get that. He's such a beast. Oh, it's not oh Teleported he just ages teleported he completely Teleported to that football. Okay.  I think he threw across line of scrimmage. It won't matter what I do here Six to zero. I would like to go into double OT with this guy so that I can redeem myself by defense So if I score here, which I plan on doing I'm gonna kick the ball just haywire. I want a double OT Yup great pass back across to Adrian Peterson. Yeah, this big Ben is fast Dude, I'm gonna block all the boys in here. Just set up some slants and let's see how this moves Can you get around the edge here? He's a little winded after that last play. Oh, I'm throwing it.   It totally didn't I got to get the throw off He was so on it. I couldn't even get the throw off. It's just a clean slate now But this is college overtime, so I can't kick a p.a.t. Anyway, I absolutely have to go for it if I get it here Let's be patient. Just be very patient. Look at a oh, I think I could have thrown that where's his D. Lyman going Rb No Wait, did you just get defensive pass interference? Wait, what illegal touching stupid? I virtually have infinite time in the pocket and at least it seems like I do I'm gonna sit both of these two down playmaker on a There he is just past it. What's going on today?  Okay number one the drop was bizarre Why did he drop it like that? Metcalfe you got to be there, baby.  Oh, I actually just blitzed my corner. Oh I actually blitzed my corner so I had to cover the corner out and that route. It was not easy All right. Well, apparently my defense is poverty and we don't deserve redemption But if we get stop here, none of it matters because that's game. Oh, it's game. It's game. It's game Let's go. I say a Simmons 14 to 12 big W. That was the best of you. Yeah, there's a lot going on there Who's gonna who's gonna catch a laser right here?  Come here. Oh, I was gonna line up on my move I had to third inches and we're going a Adrian Peterson zone alert smoke. We're giving him the handoff He knows it don't matter. Let's go and be cheese. I'm gonna look for waller here. I'm assuming this is man I can't really tell though. It is waller on the cut. He's got him.  What are you gonna do right there? I think Metcalfe might just be there. Oh, maybe not. This is bad. This isn't looking good.  That actually could have been good I was thinking he might have funneled into the end zone and then we can pick it up Big hit from Ramsey, but it's not enough because he only has to score. That's my say Can you really run it out of this formation? Not really good hit.  I've ever stopped it. Let's go fourth and goal There's no timeouts. Whatever you call. It's gotta be your play call. Gotta stay on the running back I'm gonna guard the running back with Josh Allen. I think he wants to go running back. I'm telling you. Oh, no He can't be there's no running back route. I got the right side. We're in there That's gotta be the final game big man. Look if nobody's gonna cover Big Ben's retirement I'm gonna cover it here. I'm Madden. He's you know I'm everybody's trusted reporter now that Adam Schefter now that Adam Schefter lost his credibility when I'm in a spreading Misinformation competition and my opponent is Adam Schefter. Whoa. Whoa. Whoa. Whoa. Whoa. All right boys. Hey, I love you Thanks. Watch this always what a banger.  All right. Hey, I love you boys Thanks. I just I'll see you in the next video. Peace


One of the transformations we've made along the way in our DevOps journey is changing how we do deployment. We have what we call a pre-production environment where every single change that is destined for the service has to go first. Using release management, we're able to have the individual feature teams and developers are able to trigger their own deployments and deploy hot fixes, be able to put their changes directly onto the service. Automation is a critical part of running a service. It's important across the totality of the development lifecycle from automating code check-ins and testing to make sure the code that you just checked in is actually going to work. And that enables us to have a much faster pace. You go from one change every week to two changes every week to hundreds of changes every week. I want to know about problems before my customers do. So, being able to understand what's going on on my service, being able to diagnose those issues and get it done quickly is incredibly important. Telemetry is absolutely key. We're starting to begin developing skills for understanding data and becoming data scientists, really, to be able to pick through and see the trends necessary to help us make decisions moving forward. Having the ability now to actually see what customers are doing, to instantly know if that capability that you just created and put into production is being used, that's fundamentally different. And it's really, I think, lit up the organization. Power BI is a service that's designed specifically to allow you to collect a lot of data, to sift through it, to visualize it, to create dashboards, to ask questions using natural language. Our developers are excited by the fact that they now have a direct connection with the people who are benefiting from their work. Now, having said that, because our engineers also operate the service, when something goes wrong at two o'clock in the morning, they're getting called. When we have an incident, the operations team will coordinate that incident, bring in our partner teams, do communications. But Dev's really the best suited to understand and troubleshoot their code. They're very well suited to understand those issues, and so you want to enable that access. If you don't open it up, you're going to miss out on the critical piece of that accountability and responsibility that the engineering team had. We became very metrics-driven, very scorecard-driven, but the whole breaking down the ops and the Dev responsibilities helped reduce the live site incidents more than any scorecard ever could and help just drive the accountability problem. We're seeing an improvement in terms of how people are feeling about their jobs and the impact that they're making on their customers and on the world.


When I became the digital minister of Taiwan two and a half years ago, I wrote a prayer as my job description. It goes like this. When we see the Internet of Things, let's make it the Internet of Beings. When we see Virtual Reality, let's make it a shared reality. When we see Machine Learning, let's make it Collaborative Learning. When we see User Experience, let's make it about Human Experience. And whenever we hear the singularity is near, let us always remember the plurality is here. Digital Democracy, to me, promises the potential of extending the plurality, not just about association of human beings like legal, corporate personhood, but also legal, natural personhood. Through inexpensive IoT devices, we're already seeing around the world, from New Zealand to India, people attributing the indigenous lands, the rivers and so on, and accredited personhood. This is, I think, a very novel way of us regarding the natural spirits, and making sure that whatever decision that we make as a society, we do not create externalities that are negative to the environment as a result. I think the digital technologies can really help by making everybody a participant into this global consciousness of a shared environment. And only when the rivers and the mountains are also included in the plurality of democracy, can we truly create a sustainable democracy that includes everyone on the planet, whether they're human beings or rivers or mountains.


I wonder if you could learn to keep quiet when you're in the presence of someone that you're better listening to than talking to. I wonder if you could count to five sometimes before you are about to speak and in that five second gap instead choose to listen when you would have spoken. And I think we will learn more. I think we'll be appreciated more by people. I think it will deepen our empathy and deepen our human connection if we'll learn to listen more and speak less. You know, listening isn't waiting to talk.  Some people's version of listening is they're just waiting for someone else to be quiet, then they can start talking again. Now listening isn't thinking about what you're going to say next. Listening is listening to what's been said to us and thinking about how we can interact with that in a way that enhances what's been said to us, rather than in a way that we can put out opinion and be the one that then gets to talk more than the one that's doing the talking.


Sudeep, you are next. So Sudeep's topic is Pani, P-A-N-I. Hello, you might be wondering what this Pani is about. This is not Pinenka Pani, this is something else. Pani is an acronym in Canada, and it stands for Padrasa Nirvahaka. Anybody speak Kannada here? Oh, great. It's a simple tool to manage mercurial repositories. How many of you use mercurial? Git? Wow, you all need to switch to mercurial. Okay. I wrote this simple web application called Pani. It's a simple wrapper on top of HGSSH. HGSSH is a simple SSH tool to manage user access to your mercurial repositories. So this application is written in Flask and it's open source, BSD licensed. So this is the technology stack it uses. Bootstrap and jQuery and less CSS for front end, Flask for backend. This is, of course, available on Bitbucket and also on GitHub for those who are looking to switch to mercurial from Git. I'll show you some of the screens of this application. This is from my live production system. So these are the list of my repositories and the users that have access to that repository. So what I would want from you is help. This is open source contribution. If you want to add some more features to this or even want to learn about these technologies, simply want to contribute to open source, you're welcome. I'll be, I'm available to help. So you can contact me on Twitter or send an email to me. Thank you.


The residents of Delta State have appealed to the security agencies and the state government to apply wisdom on the threats by the prescribed Indigenous people of Biafra iPop for Delta State residents to sit at home every Monday. Some of the residents who spoke with our Sabah correspondent upon Majoral Friday noted that the present situation was due to the government's failure to wake up to its responsibilities. Because the Biafra agitation is more of the emails that are agitated for the Biafra. Delta State is multi-ethnic. We have people, we have each other, we have each other, we have Delta and not Delta egos here. But the threats was, I would say the government gave these charitans the foundation because if the government has made up to its statutory duty of increasing enlightenment on their right, on what they're supposed to do. In a statement made available by the police public relations officer DSP Bright Edafi, the state's commissioner of police CP Ari Mohamed Ali, declared that there is no seat at home order in the state, assuring residents of their safety and urging them to go about their lawful businesses. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Hello and welcome to NewsClick. Today we have with us for our weekly show talk to Satyajit Rat and we are going to discuss various aspects of COVID-19. Is there any way of controlling the epidemic now in India? Why is it that we do not seem to see any falls in the numbers unlike almost all other countries which have had such large number of incases? Satyajit, looking at this issue, our numbers have been steadily rising. We do not see any fall or even in the new cases. We do not really see a dip in the cases. In the US, for instance, it is dipped twice. Of course, it is also gone up each time. Italy, there is a long dip before it rolls again, second wave as it is being called. But in India, we never saw any dip in the infections. It has been steadily going up in spite of a very draconian lockdown. And the government of India does not seem to have any response to the fact that numbers are not dipping. And we are already at number one in terms of daily infections. And if this rate continues, in another month or so, we are likely to overtake the United States as the most infected place in the world. Yes. So clearly, the situation in the two most populous countries of the world could not be more divergent. Where in one case, the pandemic, if you like, began very early at the end of the last calendar year, not even in 2020. And yet, their total numbers in China are actually less than our, at least some of our recent daily numbers. Whereas, the dramatically different trajectory that the pandemic took with us was that it started very much more slowly. One might almost say it started much later. It gained strong, enduring footholds in communities in India much, much later. And I suspect that part of the reason for that, please let us all keep in mind that much of this is guesswork of trying to make sense of a chaotic situation. But that said, our draconian nationwide lockdown for our notice implemented as a law and order policing problem quite clearly from day one will have had the effect, as we have been saying since then, of delaying establishment and foothold for the infection. Simply because people in the early weeks stayed at home with the resultant economic breakdown, with the resultant non-COVID-19 public health breakdown, not simply treating illness, but also in providing support to pregnant women for children's immunizations in all of this. Because people stayed at home, we simply delayed the takeoff. But the takeoff was always going to happen because you're never going to stop the last possibility of infection. So, the thing that we needed to have done, which we do not clearly seem to have done with any efficiency, is put in place a genuine, reliable community participating, testing to scale, contact tracing to scale, and isolation in practicable, humane, well-supported fashion. We've never done this. Our response still continues to be a policing response. One of the major prominent pieces of COVID-19 pandemic-related news in India over the past few days, for example, is that the police are collecting now X amounts of funds as fines from people who are not wearing masks. And the interesting thing there is, we're not discussing the failure of public outreach of information and true community participation in people not wearing masks. Instead, we are simply addressing how much money we've collected for non-wearing of masks. And this is emblematic of what we've been saying all these months that a public health problem is being treated as a law and order problem. When that has been the consistent approach, it's no surprise that what we are going to have is simply, as I said, a delay in the establishment of the infection in the community because of the draconian lockdown followed by an expansion of the pandemic. As the lockdown weekends are continued rise of numbers, in any case, the lockdown had to break down. You need to give people food which you were not giving. They had their poor migration. So that is in the past and since you bring it up, let's underline it again. The timetable for the unlockdown has been driven by, in part, by the socioeconomic inevitabilities that you point out that there's only so long that people can sustain it in socioeconomic terms and by a political trajectory of management of public relations, if you will, rather than responding to actual ground level pandemic situation. It's also interesting that even now it's a ministry of home that releases all the guidelines. Ministry of Health has disappeared from view. We don't even have the health minister come in on the television screens. And when the prime minister does come, he's seen with peacocks feeding them, or on Red Forb. But we don't really hear him talking about the Mahabharata war against COVID-19. We thought would be over in three weeks. But leaving that out, this is a political criticism of the public relations, leaving it out. What we don't see is any evaluation of what has happened and what we need to do now, except talking about in a very opaque fashion, lockdown and unlockdown phases and saying that now the central government is going to look after districts directly minus the state government even bypassing the state government. Again, a completely stop down centralized law and order approach to the problem. Of course, the emergency act that we have, and we are under the state of emergency because of the Disaster Management Act, which is in operation at the moment. Coming to a more specific issue, since you are in Pune and Pune has emerged as probably the large fastest going numbers of any urban area at the moment. What explains this in Pune? So as I have said repeatedly all these months, there aren't necessarily rational explanations for every event in the chaotic landscape of an epidemic. That said, let's keep what I think of as the class dimension of the epidemic in mind. The epidemic was initiated by the extent of international travel in volume terms, meaning large volume of international travel exchange. This is not to blame one person bringing a disease or anything of this sort. This is simply to point out that infection as a statistical phenomenon would spread from country to country from one original source point with likelihoods that would correlate with the volume of air travel. And the volume of air travel is driven even today by and large by business considerations. So in a certain sense, it is the socioeconomic class category of business travelers who are in a statistical sense, the transmitters across international boundaries. But once you have the virus beginning to spread locally, the local spread depends on a completely different set of socioeconomic conditions. It depends on crowding and not just crowding as in the marketplace, but crowding over a full 24-hour period with housing very close to each other, with crowding within houses, with shared toilet facilities, with extraordinarily narrow lanes, and so on and so forth. Now, if you think about it in those terms, then the largest labour concentration, urban labour concentration, which lives in India in the most crowded hutment colony conditions or tenement colony conditions, what in the Mumbai Pune sector would be called Jhopadpatti, which in Delhi would be called the Chugi Jhopadi residential conditions, or one step above built crowded flats, but with shared toilet facilities that are called the Challs in the Mumbai Pune sector, which are essentially tenement houses. The Mumbai Pune urban conglomerate is the largest urban hutment and tenement colony community in the country connected to the largest international transit point of travel. Which is Mumbai. The extent of crowding is much more in quantitative terms in a variety of parameters than in Delhi. So, for me, it's not surprising that it's over this sector that steady relentless growth, please keep in mind that the relentless growth is also something of an artifact of our political definitions. The virus is still popping up in different small neighbourhoods and dying down and popping up and dying down, but it's all happening within this, what I'm calling, in extremely high density working class communities. That's a very interesting point that you're making. Of course, if you look at the map of Maharashtra, you will see that Mumbai, Pune and Thane in between, of course, are the worst affected. And then you will see the districts which are contiguous to this spreading either up or down from these areas. So, you get a sort of a ink stain with focus in Mumbai, focus in Thane and focus in Pune, which are the urban concentrations. And the general spread of this ink blot equivalent infections. So, you do see this kind of shift that is taking place in the country. But what is worrying at the moment that the Indian spread is now almost in all parts of the country. Maybe numbers are less, maybe numbers are more, but there is no area which is not affected by people who are already confirmed cases. So, almost all areas have confirmed cases and all of them have some core areas from which there, of course, the connections exist within the urban and rural areas and they are spreading. So, therefore, unless we work out something which we have still not done, if we wait for nature to do its part, it's probably going to take years, if ever. So, is vaccines the only answer that we will have in terms of the containment? Otherwise go on like this. So, this is a very, this is a really distressing thing to have to say that we seem to be in a situation where our response as a state, our response as a government or as governments is simply to continue from yesterday despite the fact that the empirical evidence on the ground is that what we were doing yesterday is at the very least not adequate and yet there doesn't seem to be radical rethinking. At least none of us have heard any radical rethinking about what policies and strategies might be thought of a fresh an anew in this matter. I think that is a very depressing situation for us to be in because what that says is that this is how we are going to go on. We are going to go on with the top-down policy in which district administrations are being threatened and blamed by government leadership, by political leadership to ensure that your case numbers are low, to ensure that your deaths are low without an appropriate and adequate injection of resources, without an appropriate and adequate conceptualization of strategies and policies. All that this is going to do is, um, police size as a verb to substitute for militarize, police size the problem more and more and more with the result that we are going to see growing resentment against government responses. We are going to see growing resentment against what will increasingly be seen as distancing measures that apparently have no effect. We are going to see resentment against the inadequate, the manifestly inadequate provision of COVID-19 related medical facilities. In Pune, there are stories on the front pages of the newspapers about patients in dire need for oxygen and for intensive care facilities running from pillar to post to find admissions. We are going to see increasing resentment about this. What we are going to keep getting in response is more and more the government wielding a big stick, apparently ineffectually. And this is going to be a state of emergency that will continue and will be utilized almost perversely as an, let me quote our leadership, as an act of God to rationalize failure of the government on very broad fronts. To rationalize its failures and there are two other things they can of course do, that if for the central government blame the states and also externalize the issue. There of course is, as you know, the Shushant Rajput case, which has become according to television, the most important national issue in the country. And they have discovered that actually in the film industry, young people take marijuana and other drugs. Apparently, they were not aware of it. This is something which was used to them. So this has become the major center of major talking point across the media, huge failure of the media. You don't hear about the failure of the policies, the health policies, what's happening. All this is only coming up in numbers and only in response to what New York Times and others are writing. And still the media is dominated by other news. And of course, you have the border. So, you know, the border news, nationalism, I'm not going to go into that today. But this are good distractions because I doubt that India and China will be foolish enough to get into a border war. But nevertheless, it keeps the pot boiling. And it's also true that there is no hard questions being asked in general about the failure of the government. And let's see this, even Brazil, even the United States, their numbers are falling, India's numbers not. So that is, that's really something which should worry everybody. But I don't see this worry either in the government or in the quote unquote informed circles, which is the first layer of this media. Yeah, so you're absolutely right. And it's interesting that you bring up the contrast between the recent situation of the pandemic in the US and India, and the distinction seems to be on two major fronts. One, as you point out, there are sections of the media in the US by no means all the media, but there are sections of the media in the US, which are doing systematic in depth reporting. And keeping the pandemic in the public eye. And that's sadly helped by the fact that the numbers in the US are so dramatically more even now. But the second issue, which again relates to governmental policies in response to the pandemic, is that there are in the federal structure of the US, there are state governments that have responded quite differently from the responses of the federal government to local control of the pandemic, to outreach for distancing measures, participation in distancing measures, to test provision to contact tracing, those sorts of situations. They're not huge differences. But those small differences, I think accumulate into the current differences between the two countries. We do not seem to have, as yet, with an occasional exception such as Kerala's models, that dramatic is separation between the response of the union government and the response of state governments in part, of course, driven by top-down approaches all around. Yes, I think that's where you also break out the fact that the media, at least in the United States, has turned the light on to the failures of the government. And in our case, that media has played a very, very quote unquote reporting role, but not an investigation, is investigative or a critical role that it is also supposed to play. And particularly give space to those voices. There are there public health experts who are questioning all of that, but their voices are really not percolating. And what seems to come is things which are avoided, deaths, our death numbers are actually lower compared to the number of infections, etc., etc., which we can discuss another day, but essentially, alibis of some kind or the other. Thank you, Satyajit, for being with us as you have been for quite some time now, and we'll come back to you next time probably with looking at the vaccine issue again. Because as we have said here, the only long-term solution that lies now for India, more than even for other countries, is the vaccine without which we don't seem to be able to come out of the crisis we are in. This is all the time we have for NewsClick today. 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So today on Anabaptist Perspectives, we're here with John D. Martin and we are discussing the Christian view of economics. So John, right into the first question here, describe the economic vision that emerges from the Bible and Christian orthodoxy. Perhaps you could start with a key passage from the Old Testament and maybe one from the New Testament. Well, God has always been concerned that we understand that we don't own anything. He owns it all. And so you go to the Old Testament and you go to Leviticus 25 and you have what people call the Jubilee principle where God had built into his commands to his people that the land belonged to him and that everything belonged to him. And at the end of seven years, there was a partial readjustment of everything because God knew that people would tend to accumulate. And so he built into his economic plan for his people for there to be a check on that. And then every 50 years, all of the land went back to the original families, the families were given portions of land, which they did not own.  But in every 50 years, it all got leveled off. And everybody started back at the beginning with all the equal opportunities. And in fact, the idea of withdrawing from the material to think about the reality of God is built into the concept of the rest on every seven days. I know there's a lot of differences of opinion about how that applies in the Gospel here, but there's a principle there that we take time to back away from the material world, take a vacation and concentrate on the things that really matter and remind ourselves that none of this stuff belongs to us. This material work is temporary, that the real is something else and it all belongs to God. So you get that principle right away in the Old Testament, you get it right at the beginning with the seventh rest every seven days. So that's just a principle that God built into His Word and that is that nothing belongs to us and we tend to forget that. And so God built these reminders into the Old Testament. So that's where I would focus in the Old Testament on the Jubilee principle and God's concern that we would literally back away from this stuff and just take a vacation. What would you see as a key passage in the New Testament then that follows up on that principle? Well, Jesus said, don't lay up treasures on earth, lay up treasures in heaven. And so that's the Gospel principle that we are not supposed to accumulate things on this earth. And with the other commands that Jesus gave, love your enemies, pray in secret, these are just commands where He doesn't explain, but I think Jesus knew that we would need more understanding, more motivation in this area because I think He realized that this was the one great rival against Jesus' wealth. You cannot serve God and mammon. He knew that the most popular rival that Jesus had is stuff, our possessions. And so when He gives the command, don't lay up treasures on earth, He takes time to explain why. He doesn't do that, at least as much with His other commands, but He does. He really gets into this. He says, don't lay up treasures on earth because where your treasure is, that's where your heart's going to be. Don't ever say, I have all this stuff, but my heart's not there, which is a popular way people try to handle this. He said, no. He said, show me your pile of stuff and that's where your heart is going to be. That's just the way it is. In fact, Peter says, your heart, you have exercised with covetous practices. Our heart is conditioned by the way we handle our things. And so we just constantly need to live with that understanding that what we're doing with our stuff is affecting our heart and our sense of values. Now, I want to say a little word about values. Most of Christianity gets defined in terms of morals, what is right and what is wrong. And Christians are intensely concerned about morals. I'm not minimizing that. Christians, they don't lie. They don't cheat. They don't fornicate. They don't, right, that morals, that's good. But there's a dimension beyond morals that God wants us to be, I would say, a justice concerned about in a way maybe even more concerned about because I think morals come out of this area, which is values. And values has not to do with what's right and what's wrong. Values has to do with what is important and what is of lesser importance. And it's interesting to me that when you go into Hebrews chapter 11, there's nothing said there in that faith chapter about morals, nothing. There's nothing in Hebrews 11 about the morals of those people. It's all about their values. And it's interesting to me that that's the faith chapter. And so I really do believe that faith is probably going to be that most determined by the value system that we have. Are we valuing the stuff on this earth? Are we valuing something, valuing something, valuing something else? The Bible says that Abraham, he valued something he couldn't even see. And in his lifetime, he didn't see much of it. He only saw a few descendants. But he valued, he looked for a city that has foundations, who's builder and maker is God. And so when we're talking about this, we're talking about values, which I think is vitally connected with our faith. I mean, our faith is also connected with our morals, but I think it's even more determined by our value system. Do we value the invisible or do we value the visible, which is transitory? So Jesus said, where your treasure is, that's where your heart is going to be. The second thing he said is the light of the body is the eye. Now that's often taken out of context, but this is right here in the subject of what Jesus said about possessions.  You're not going to see things right. You're not going to make good decisions. You're going to make temporary decisions about transitory things. In fact, Peter talks about not being able to see a far off. To be spiritually blind is not to be without any sight at all. It's when you don't see things rightly. And if you're going to focus on material things, you will not see a far off. You will see things in the immediate present, and you will lack that kind of distant vision that Christians need to live by. So Jesus said, the light of the body is the eye.  And that's an interesting comment there in the Old Testament.  And so I really do believe the reason why many wrong decisions get made at the individual level, at the church level, is because people have not dealt with this whole issue of possessions. And Jesus said, don't lay up treasures on earth. Your heart's going to be where your treasure is, and it's going to affect your ability to see. And then the last thing he said is, you cannot serve God and mammon. You can't serve both, because this has to do with worship. The word worship is the Old English word for worship, W-O-R-T-H-S-H-I-P. The word we use is a contraction. It has the T-H taken out of it. So worship had to do with values. Now we're back to values. Worship has to do with what you value most. Whatever that is, that's what you're worshiping. And many people who say that they're worshiping Jesus, if you listen to them, they're very passionate about everything but Jesus. They're passionate about their money. They're passionate about their vacations. They're passionate about their pleasures. They're passionate. They're passionate about all kinds of things. But I think a person's values is most indicated by his passion. What you're the most excited about is the thing that you value. And for most people who say they're Christians, that isn't Jesus. It's their stuff. And so then now we're back to values and faith and all of that. Values are very important and that's why I think this is such a huge issue. In fact, there are 38 parables in the New Testament, 16 of them, almost half of them have to do with money and possessions. Somebody has said one out of 10 verses in the Gospels has to do with money. That's 288 verses in all how. In the whole Bible, there are 500 verses on prayer. There are 200 verses on faith and there are 2,000 verses on money and possessions. So this is a very important subject. In fact, I don't think that the Gospel addresses any subject more than this except the kingdom of God itself. But this is a far greater subject in focus in the Gospels than any other subject. And that's why it's a mystery to me that so little focus has been on this. I grew up in churches where I don't think I ever heard a sermon on it. If you heard a sermon on stewardship, it was about investing your money and making it gross and you'd had more to give. It was sort of a gospel defense of the American dream from the parable of the talents. But I don't think I ever heard a sermon that you should do what Jesus did, though he were rich yet he became poor. He voluntarily let that go so that he could focus on the things that need to be focused on. So that's what you have. Don't lay up treasures on earth because it will affect your heart. It will register as a desire that will overshadow other desires. Number two, if you accumulate wealth on this earth, you will be focused on the temporal. You'll be focused on the material. You will not be able to see a far off. You will not see right. You will not make long range decisions. And number three, you cannot really worship if you're focused on things. Well, you'll be worshiping, but you'll be worshiping them because you cannot serve God and mammon. You will worship the thing that has the most worth to you, not in words, but in actuality. And so that's why this is a very important subject for everybody to get right. So their value system is right. So they're men of faith. So they're men who can see. So they're truly worshiping God. So yes, that's my answer, long answer to your short question. Well, that's really hopeful, though, to get like the context of where you're coming from, because this next question is going to touch on something specific of that. You've spoken in the past about how inequality as iniquity, or there's something to that effect in some of your other sermons. So in anticipation of common criticisms, could you respond to the idea that fairness and justice are always out of reach since, to quote Jesus, the poor you will always have with you. And if anyone is unwilling to work, he shall not eat. How would you respond to people that might have those criticisms? We are taught that we are to work to provide for practical needs, not only our own, but our relatives. As far as the poor of the world, yes, we will never meet all the needs and the focus is not on whether we meet all the needs of the poor. The focus is on what are we doing with our resources, the focus is on what we're doing, not necessarily how much of the need gets met. We will never meet all the needs, of course. But the focus is on what am I doing with the resources that I have. So this whole inequality as iniquity aspects, that's what you're saying. You're hitting it more from the angle of what are we doing instead of the fact that, hey, there's inequality in the world. Therefore, there's something wrong with us. Well, let me back up just a little bit. We are talking about Christian community. We believe in two kingdoms. We believe the local congregation is the practical expression, as far as we're concerned, of the kingdom of God here and now in our situation. And yes, equality in that society should be of great concern to us. There should be no poor in the church. There should be no rich in the church. There should be fluid assets. 2 Corinthians 8 verse 15 clearly lays it out that your abundance may serve for their need. Their abundance may serve. And so the assets we have as a brotherhood should be fluid wherever the need is. That's where the money goes. Wherever the money is, that's where the need is going to be met. And yeah, there should really be equality. That would never be perfect, of course. But that should be our passion. Our passion should be that if somebody came to visit our churches, they would visit around and they'd say, well, the houses are about the same. The cars are about the same. People seem to have their needs met. There are no poor people here. There are no rich people here. That should be our testimony. Okay, so that statement, you're not necessarily applying it to the whole planet because there's inequality financially among many different people. You're not saying quite so much. You're focusing in on the church and ourselves and how we're living. Well, I would say that the church and individual Christians would have a passion to create as much. I hate to use the word equality because that's never really going to happen. But to see that as much of our resources as possible gets to as many needs of the world as possible. Yes. In fact, it was estimated that I think, I don't know if I had this figure right, that $15 billion would basically provide the nutrition for every person in the world and there'd be nobody starving. Now, $15 billion is a lot of money. But when you think in terms of the kind of money that, you know, we have as a nation, when you think even of the kind of money we have as a Mennonite church, that figure's not too far out of reach that we could actually feed every person in the world. That's not an awful lot of money in terms of national economics. So that gives a little bit of an idea of what's happening. There's a lot of money concentrated at one place that doesn't get shared. And Jesus wants to completely destroy that mentality. God never wanted that to happen. That's very helpful clarification. I'm curious what our audience is going to think. And if they have questions, y'all can leave them down in the comments below. But as we wrap up this section here, could you name a few resources you would recommend for people to get a better understanding of what you've been describing here? I have not studied this subject much outside the Bible itself, but I would recommend Roger Hertzler's book, Through the Eye of a Needle. James Steyer is a man who did a lot of study on enabaptist economics. He wrote a piece, I don't know if it's a book or if it's an essay. I know he did write a book on the subject, but I don't know the title of the book, Economics and the Enabaptist. That would be good to read for our own people. But no, I don't have a lot of resources. Sure. Outside of start studying the Bible and see what it says. You know, Mennonites, if there's one verse in the Bible, they'll go all out to obey that one verse. If they would just actually listen to what Jesus said and quit rationalizing and quit saying, it doesn't say where your treasure is there, where your heart be also. What it really says is where your heart is. That's where your treasure is. So you can have a huge Apollo stuff and tell people your heart isn't there. That's the kind of theological gymnastics that we like to do with what Jesus actually said. Or he told that rich man to sell his stuff because he had a special problem, which of course we don't have. So that's why God had to tell him that. The interesting thing is in Luke chapter 12, he says the very same thing, sell that you have and give alms, provide for yourself bags, switch waxes, not all that treasure in the heaven that fail if not. And he's talking to everybody. He's talking to the disciples. He's not just talking to the rich man. So, but that's the kind of stuff we do with the parables. We take the, we, or with Jesus' teaching, we take the talents and well, Jesus says you're supposed to invest and make things increase. And we just totally overlook everything else he said on the subject. Wow, that's really helpful. That's a really neat perspective. Thank you for coming on and sharing this with me and the rest of our audience. And yeah, thank you all for watching and we'll catch you in the next episode. Thank you for joining us for this episode. And thanks to our donors and partners for making this possible. For more episodes, please subscribe or visit our website at anabaptistperspectives.org. You can also leave a comment or review to help more people find our content.