First, I will present the summary paper of the Blizzard Challenge 2020. Okay, this challenge, as we have mentioned, this is the 16th annual Blizzard Challenge and the first challenge was held in 2005. And this is the first challenge that was organized by the team at the University of Science and Technology of China. And this year, we released two datasets of Chinese dialects for evaluation, and all evaluations were conducted online. In terms of the techniques adopted by the systems, we can see a clear trend toward using neural network models and neural decoders. I will present more details in the following slides. Okay, for designing the tasks, I founded a company, generously released, provided two datasets for system construction. The first dataset is a Mandarin data speech dataset. The transcriptions are daily news. A speaker is a professional male native Mandarin speaker. The total duration is about 9.5 hours, and the sampling rate is 48 kHz. We provide audio together with text transcriptions. Another dataset is a Shanghai news, considering that this workshop was originally planned to be held in Shanghai. So this dataset, the transcriptions are also daily news. The speaker is a professional female native Shanghai news speaker. It's a 3-hour, 16 kHz sampling rate. In addition to audio and text transcriptions, we also provided phonetic transcriptions for both the training and the test data of this Shanghai news dataset. Based on these two datasets, two tasks are designed. The first is the half task, which is to build one voice in Mandarin using the provided data, and each participant is required to synthesize the test set of 700 utterances, including news sentences, PSC sentences, and intelligibility sentences. The PSC sentences are sentences of the Mandarin proficiency test in China, and the intelligibility sentences are meaningless ones and composed by random choosing words. For the spoke task, it is to build one voice in Shanghai news using the provided data, and each participant is asked to synthesize the test set of 391 utterances, including news and chat sentences. The rules this year just follow the rules of previous challenges, and we just made one modification. That is, we put a limitation on the amount of external data. That means each participant must use no more than 100 hours of audio for each task, including the provided data. This challenge has attracted 32 teams to register and download the data. Among them, 16 teams submit the half task results, and the eight teams submit the spoke task results. We can see a very balanced distribution between academic and industry teams. Following previous challenges, all systems are identified using anonymous letters during the evaluation, and the letter A is used for natural speech, and we have no benchmark system this year. All systems this year follow the neural network-based statistical parametric species approach. That means that is for the first time in the Chinese history, there were no feedback model based, or unit selection and web home companion systems were submitted. Among the 16 half task submissions, 11 teams adopted the sequence of models, such as TechControl, and the five teams adopted DMBaseX models. And neural recorders were adopted by all systems, with WebIron, WebNet, and LPCNet being popular choices. And 10 of the 16 teams utilized external data, such as the DataBaker dataset, which is a Chinese open dataset, and an LG speech, like this dataset. Okay, let's go to the listening test. We have just like previous challenges for the half task. We have three types of listeners, including the paid university students, and the speech experts, and the volunteers. For the spoken task, because it is Shanghai, we just had the paid university students. They are recruited at the Shanghai International Studies University. So due to the impact of COVID-19, all listeners completed the test online this year, because it prevents the challenge. Usually, the paid listeners will conduct the test outside. But this year, all the listeners conducted the test online. So this is how the test is designed. Actually, we only select a subset of the completed test set, and use them in the formal listening test. For the half task, there are six sections. Each section has 17 samples. These six sessions cover the similarity, naturalness, paragraph, and the intelligibility. For the spoken task, there are seven sections, and nine samples in each section, which covers similarity, naturalness, and intelligibility. It should be noticed that considering the complexity of input transcriptions for Shanghai needs, the dictation test was not conducted for the intelligibility test of the spoken task. Instead, the listeners were asked to choose a response that represented how intelligible the synthetic voice was using a scale from 1 to 5, just like the mean-opening score test. The overall completion rate of the registered listeners was 84.8%, which is quite high this year. And we also ignored some results, which seems not very good. Finally, we have the numbers of listeners used in the final listening test result. There were 370 for the mean task, and 87 for the sub-task. Okay, let's go to the results of the challenge. At first, let's look at the mean task. This figure shows the box plot of the mean-opening scores on naturalness of all systems. Here, letter A indicates the natural speech, the other systems are ranked in a descending order of the mean-naturalness. We can also see some colored symbols below each system letter, which indicates what kind of acoustic models and neural encoders were used by them. So, at first, I'd like to play a sample synthesized by all these systems. You can hear them one by one. Can you hear the sample? We can, but the audio has stopped. Stop? Okay, maybe I can restart it. Yes, please. We can, but the audio has stopped. Stop? Okay, maybe I can restart it. Yes, please. 60% 60% 60% 60% 60% 但是收取的费用最高不得超过 60% 但是收取的费用最高不得超过 60% 但是收取的费用最高不得超过 60% 收取的费用最高不得超过60%, 但是收取的费用最高不得超过60%. OK, so as a native speaker of Mandarin Chinese, I can see that most of this sample sounds quite good. But there are still no system was as natural as the reference natural speech according to the results of significance analysis. And among these systems, system AI and system O were significantly more natural than all other systems. Their new opening score was 4.2. Let's look at the techniques used by the systems from these labels. We can see that there is no clear conclusion that if the synthesis model could perform better than the DNBAS model, because there are some for the top system, some use the DNBAS model, some use the synthesis models. Regarding with the vocoder, I think most of the systems use the autoregressive neural vocoders. Only two systems use the non-autoregressive ones. One is the WebGlow, another is the PWG here. So for the top systems, most of them use the WebIN and WebNet as their neural vocoders. OK, let's continue. This figure shows the similarity results. We have some conclusions here. No system was as similar to the target speaker as the natural speech, and the system AI was significantly more similar to the target speaker than all other systems except system O. And its similarity of system AI in the new opening score is 4.2. For the intelligibility, this figure shows the pinging error rates with tones according to the dictation results of listeners. We can see that there are several systems achieved the insignificant difference with the natural speech. There are system D, here, I, L and P. There are no significant difference among them and the natural speech in terms of the integrity. OK, then let's move to the spoken task. This figure is also the naturalist results showing box plot. We have eight submissions together with natural speech. These labels indicate their methods. At first, I will also play a sample. 紐約互相互作臉文不落散黑心色比起 紐約互相互作臉文不落散黑心色比起 紐約互相互作臉文不落散黑心色比起 According to the listening test results, there were no systems as natural as the reference natural speech. And the system AI was significantly more natural than all other systems. Its mean opening score is 4.0 on naturalness. For similarity, no system was as similar to the target speaker as natural speech. And the system E achieved a mean opening score of 4.1. It was significantly more similar to the target speaker except system L, that is this one. For intelligibility, as I have said that we didn't use dictation but used a more mean opening score test for the intelligibility in relation of the spoken task. We can see that there was no significant difference between the system I and E. For system I, its intelligibility has a median score here of 5. It is the same as natural speech. But a further investigation shows that we calculate the Pearson correlation coefficient between the naturalness and the intelligible scores of all systems. We can find a strong correlation. The correlation is about 0.76. This means that you use this kind of more intelligible test. When listeners make their judgment, they may also be influenced by the naturalness of the speech. So maybe in the future, we should prefer to use the dictation test. Okay, let's go to the summary of this paper. So it has been eight months from the announcement to this workshop. In this challenge, we have designed two tasks and we have all together 16 team submitted their results and we have recruited more than 500 listeners. And the best naturalness and the similarity scores of the best system, that is 4.2 on the half task. Okay, so if you are interested in more material of this challenge, we can find the... Maybe we haven't done that, but we will do it in the near future. We will put the listening test results and the synthetic speech materials to the bridge challenge website. And all the bridge challenge papers are also available at the FETS Fest Vox website. So yeah, I also want to thank a lot of people who helped us to organize this challenge. Thank you very much, especially to all the participants, to all the teams and all the listeners. Thank you very much. Okay, thank you. So this is the presentation of the summary paper of this challenge. Now, I think we still have two minutes for questions. So if you have any questions, you can type them. Hello, everyone. My name is Leipeng from the Unison AI Technology Cooperation. Today, I will give you a brief introduction about what we have done for the Blizzard Challenge 2017. We are team I and most members are my colleagues as well as mates from Shanghai Normal University. This is our first time participating in the challenge. We built two TTS systems for both Mandarin and Shanghai news tasks with the same architecture. I will divide the presentation into four parts and now let's begin. Usually the front-end module for Mandarin TTS consists of multiple sub-modules as shown on the slides. This time we used a rule-based TN module to convert non-standard forms of words into spoken forms. Meanwhile, we applied two CIF models for word segmentation and part of speech tagging respectively. G2P module converts Chinese characters into PN sequence through looking up a dictionary. However, sometimes pronunciations are difficult to determine such as those for polyphones. We applied CIF models for the most frequently used polyphones to predict their pronunciations. The same method was also used to predict the boundary of a positive word and phrase. Word form, part of speech, and word length were used as input to predict the boundary of a positive word. When dealing with a positive phrase, a positive word boundary was also taken into account. In Mandarin, TTS and Erhua sound will significantly affect the intelligibility. Our system used grammar rules and prosthetic paths to handle these special situations, such as the third TTS and the sound of Yi and Bu. For Erhua sound, first of all, we listed all the non-Erhua sound words, so the system could read those words, which are not on the list in the right way. A tectron-based 626 model was used to predict male spectrum from character sequences. Here, the CBHG was retained as a post-processing module to further improve the prediction accuracy. Chinese characters were first converted to PN sequence and further represented by international pronunciation alphabet. Prosthetic word and phrase boundary representations were inserted between IPA characters. A trainable embedding table was selected to store speaker embedding. For Mandarin data, each paragraph was equipped with an embedding for style modeling. We used an autoregressive webnet to reconstruct audio waveform. Audio samples were transformed by 10-bit mule and context. Speaker embedding and male spectrum were used as global and local conditioning, respectively. Deleted convolutions were adopted and the filter width was set to 2. The training data was annotated before training section. Some extremely long sentences were divided into multiple classes. Prosthetic word and prosthetic phrase boundary annotations were performed on Mandarin data. Silence was trained to further improve the stability of attention, both at the beginning and the end of a sentence. What's more, we have sampled the Shanghai audio to a sample rate of 24 kHz. The high frequency band was a fake spectrogram generated from the original audio. At the training phase, we first trained a tectron webnet system based on our 90-hour corpus. Mandarin and Shanghainese corpus were adopted to perform fan tuning based on the pre-trained models. Male spectrum used for webnet fan tuning was generated from tectron via ground-truth alarm mode. One thing to mention is that our training corpus doesn't contain any Shanghai news data, but the experiment proved that the above training scheme was also effective, which means the training data of Mandarin was also helpful for Shanghainese training. At the census phase, Mandarin test sentences were first sent to the front-end model to get PN sequence and prosthetic boundaries. As for Shanghainese, the phonetic transcriptions were used as input of tectron. Global variance conversion was performed on the generated male spectrum to adjust its dynamic range. Now, after my introduction, let's take a look at the results of the system evaluation. Systems are evaluated from four dimensions, and the following is mainly about our results in each test. In the natural needs test, the most for our Mandarin system is 4.2. And now let's listen to Assistant's speech from AutoDomain Center. More than 300 years ago, the architecture designer, Lai Yi'en, designed the English-language government hall. He used the knowledge of engineering history to design a hallboard that only uses a pillar of support. A year later, the government was able to complete the engineering test, but it was too dangerous to use a pillar of support. Lai Yi'en asked Lai Yi'en to add a few more pillars. Lai Yi'en believed that only a pillar of support was enough to ensure the safety of the hallboard. Lai Yi'en's stubbornness made the government official seem to be sent to court. Okay, the boss of our Shanghainese system is 4.0. And here is Assistant's speech. The next part. Okay, the next part. This part evaluates the similarity between Assistant's speech and the reference recording in the Mandarin task. Our system obtained an average opinion similarity score of 4.2. For the Shanghainese task, our system scored 3.6. The intelligibility test for Mandarin requires listeners to record the sound. The opinion error rate with tons of our system is 9%. Moreover, our system solves most of the Erhua sound problems and less fuel it by a fuel sentence. The next part. Okay, the process for Shanghainese uses a scoring mechanism similar to naturalness and similarity. And our most score is 4.1. The paragraph test was to evaluate Assistant's speech from multiple aspects. In each part, listeners were asked to listen to a whole paragraph from news before choosing a score from 1 to 60 for each aspect. And the main opinion scores of our system are listed in the table. Evaluation results have proved the effectiveness of our systems at the same time. Our systems are not perfect and there are still some problems. We use CF models to solve most of the frontend problems for simplicity. But its prediction accuracy is limited and errors accumulate between modules. The structure of texturing leads to pronunciation problems such as word repeating and skipping. In addition, we use an autoregressive WebNet model as a record. But the influence speed is really slow. We have tried many ways to improve the above problems. And our new solution now has solved the instability problem and it runs much faster than real time. In fact, the engine has been released for commercial use. Okay, that's all about our systems in this challenge. Thanks for your patience and listening. I'll be coming to introduce you to the Opos text to the system this year. First, let me play a few audio clips from our submission. These audios are generated from different types of text in the competition. For example, in Jilinsheng, the unlimited power monitoring device has achieved full coverage. How can the world cup have such a huge吸引 power? Except for its own charm, what else is more powerful than that? Let's think about it. Okay, next let me explain the overall architecture and the frontend pipeline. Like the other system, our system consists of two phases, the training phase and the testing phase. In the training phase, we extracted acoustic and somatic features from the prepared data set. And then we use those features to train our frontend model, acoustic model and the recorder in sequence. In the testing phase, we use the trained frontend and acoustic model to generate features in sequence. And finally, the audio is generated by the recorder. That's our overall architecture. And I'm going to go through the frontend module, the acoustic module and the recorder in turn. The goal of our frontend system is to generate accurate phonetic and prosthetic labels from the input text. These pictures show the overview of our frontend pipeline. First, all the text will go through a text normalization module which transforms the text into a standard writing form. Next, we hand over the normalized text to BERT for a charm level, feature extraction, which extracts features that can turn richer context information from a character sequence. In our experiment, it can bring a significant improvement compared with traditional charm embeddings. Then we use the extracted char features to do word segmentation and part of speech tagging. And further extract word novel features and post features from the results. Finally, we use all those features to train our prosody model and G2P model and get a prosthetic label sequence and a phone label sequence. Now I'm going to talk about how we use the feature extracted by BERT to make our frontend more accurate. There are currently two basic modeling units. One is based on characters and the other is based on words. We found that the word-based approach is more precise because words literally carry certain prosthetic boundary information. But it is easy to be affected by the results of word segmentation. It means that if your word segmentation system makes a mistake, the prosody will sound bad. So we use the character-based method, which is simple and flexible and is a lot affected by the result of word segmentation. We appended the word information to the corresponding characters at the same time. In this way, we returned the word information and alleviated the problem that the result was affected by the word segmentation result. So the question becomes how do we combine word features and character features? We will introduce a core idea of our method through a simple example. As you can see, we have a word sanzang, which means rice in English. Or you can call it a char sequence. It consists of two char, which are sanzang and zang. First, we can use bird to extract features from the char sequence. In this case, since we have two char, we can get a char level feature with two time steps. Then for this word, we repeated trance to get a sequence of two words, which guarantees that two char are able to get the word information. Of course, if your word contains three char, you'll need to repeat the char three times to get a sequence of three words. Since there is a low word-based pre-trained bird, we use a traditional pre-trained word embedding instead. Finally, we can calculate the word features, which is from word embedding and char features, which is from bird, to form a new feature at input to the processing model and the GZ model. And now it's our processing model. We still adopted a hierarchical structure. We mainly use prosodic word level and prosodic phrase level. We represent the prosodic label equivalence as two-level boundary labels. Therefore, we need two different levels of feature extractors, which we represent simply use NPW and NPPH. As you can see from the diagram that we have an input, the input here is the concatenate embeddings mentioned in last slide. We fit the input into two-level neural network feature extractor, get the respective boundary result, and then combine the results into the fellow prosodic labels. One thing that needs to be said here. The output of NPW will be concatenated with original inputs again at the input of NPPH so that the hierarchical information can be passed. And can be any feature extractor, like transformers, LSTM, we use transformers. And that's all our front-ended related works. And we use type 2 to predict the mirror spectrum. For Mandarin, we build a multi-speaker model with a five-mile Mandarin Chinese datasets and one-mile English datasets. In order to preserve the characteristics of each speaker, we add a 16-dimension speaker embedding table and concatenate the speaker embedding with the original outputs or encoder to form new inputs for the decoder. Then we use the converged multi-speaker model as the initial model to find two on competition datasets. For improving the robustness of long sentence TMM attention with eight mixtures was used. At the same time, we use the guided attention loss for the convergence or alignment. In the end, we adopt WaveRM as a work order and 25-hour Mandarin mile corpus was used to get the base model. Then we fine-tuned the model with the provided data. Finally, we fine-tuned the model with GTA ground truth line. So WaveRM structure was used in similar to the original paper but with several improvements. Instead of the GRU or dual-softmax layer to predict the fine part and the coarse part, two GRU layer and three dense layer are used. Thank you. That's our assistant. Thank you for your listening. Hello everyone. My name is Zou Wang Zhang and it's my great honor to give a presentation about the Tencent system for Blizzard Challenge 2020. Blizzard Challenge 2020 has two tasks this year. The first task is the MHU1 task. It contains nine hours of Mandarin Chinese speech data from professional male speaker. It provides text transcription and audio files. The second task is SS1. It contains three hours of speech data from a native Shanghai famous speaker. We are provided with text transcription, formings and audio files. Here is an overview of our TDS system. We have three parts. The first part is front-end analysis and we have a very robust front-end project supporting both Chinese and English. The second part is acoustic modeling. We have two systems for Blizzard Challenge. The first one is add-during system. It's a variant of a durian system proposed by Tencent AI Lab and it's for MHU1 task. The second system is GMM way-to-be-based text transcription for SS1 task. The last part is neural coder. We adopted the Federal Wave. It's proposed by our team and it's a variant of Wave Rn. The right figure is the overview of our TDS system. First, let me introduce our front-end analysis. We have customers, labor and SSML. Besides, we have text normalization. It's a neural-based and model-based. The model is bidirectional, error-steam and MRP. We also have English segmentation and Chinese segmentation. The English segmentation is based on trigram model. The Chinese segmentation is based on bidirectional, error-steam and CRF. We also have PIN detection and letters detection. It decides how to pronounce, when met with PIN or letters. Besides, we also have procedure structure prediction. It's based on bidirectional representation and error-steam and CRF. Besides, we also have polyform disambiguation for Chinese. It's based on MMT encoder or bot and MRP. We have just one model for all Chinese words, for all Chinese polyform words. Besides, we also consider the tone sampling. We also have English G2P model. It's based on word level, sequence to sequence. The red figure is the framework of our front-end analysis. The next part is acoustic modeling. The first is adduent model for MH1 task. We have foaming and tone stress and prosthetic symbol as input. We also need HMM-based hard-aligned duration to give guidance for acoustic modeling. We have skip encoder for robust pronunciation. We also apply speak identity style code and language decoder for more robust and controllable expressive TDS. The decoder contains two residual L-steams. We also have time-delayed L-steam post-net. The next system is GMM W2P-based detection 2. It's for SS1 task. It has fast alignment and very good stability. It doesn't need any hard-aligned duration and prosthetic. It can learn end to end. The red figure is our adduent system. The following question is from GMM W2P paper. The last part is neural encoder. As a climate in the paper, W1 2048 can get no significant difference with the WebNet in most tests. Bigger W1 with GIO hidden size 2048 was used by us to obtain high-quality generative speech. The W1 model at 48 kHz sampling rate as offered in the task MH1 for high-fidelity speech synthesis, which conditioned on conventional 24 kHz mere spectrograms produced by acoustic model. For SS1 task, 16 kHz W1 was adopted as a corpus sampling at 16 kHz. The first neural encoder adopted the other variant of W1. We used 12-bit new law and four bands to efficiently model discretized waveforms and faster generation. Conditional network consists of one-time-three convolution layers with channel size 512. Before passing into sampled network, extracted local features are repeated to match the sampling rate of target waveforms. Finally, as acoustic features are extracted with hop size 240 from 24 kHz audio in task MH1, local features are repeated 480 times to generate 48 kHz waveforms. If you want to know more details, you can refer to the paper. Here are some samples of our system. In order to ensure that the 2018 high-fidelity test is successfully completed, Huawen will definitely be the one who will be the head of the test. In order to ensure that the 2018 high-fidelity test is completed, let me introduce the evaluation results. The first evaluation is naturalness. As you can see, the system OIR performs better than other systems. It's proved that our adherence system has shown superiority over most other systems. The next is the similarity test. In this section, each listener should score the same set of audio in two fixed-reference samples, and our system achieves a third highest score and shows significant advantages than many other participants. The third test is pin-in error rate with tone test. In this case, system IERD has a similar performance, and our system IERD has the lowest error rate. It's all into our adherence system. The second part is SS1 task. The first is naturalness. The top three systems IERD perform similarly. It's proved that our tecton-based speech sensor system has shown superiority over most other systems. The next one is speaker similarity. And obviously, system E performs best, and our system IERD achieves a second highest score and still performs better than many other systems. The last one is inter-gability. In this test, system IERD performs much better than other participants. Our system IERD still has a third highest score of inter-gability. Let me draw the conclusion. We have three parts, front-end analysis, acoustic model, and neural recorder. We have very robust texanomalization, very high accuracy of polyform design, big-view action, and very natural prosthetic structure predictions. As for acoustic model, we have add-during and GMA-V2B-based tecton. We have sub-gibbing encoder for robust neural TDS and tongue embedding for more accurate pronunciation. Stereocode for more controllable and expressive TDS. Pertrained multi-speaker-based model for stability. This is for image-one task. And finally, the GMA-V2B-based attention for stable and fast end-to-end learning. Neural-coder Feather Wave is based on 12-bit neon law for better quality and LPC and multi-band processing for faster inference speed. That's all. Thank you. Hi, everyone. My name is Zhixing. On behalf of our team, I'm here to present our TTS system built for the 2020 Belize challenge. Here I will briefly introduce the technique we applied in our entry. We rely a lot on neural network models to meet the goal of the challenge this year. The synthesis timeline is shown in A from the figure on the left. Prosody annotations are added into the test sequence by the prosody prediction module, which is designed to predict appropriate policies within speech. Then the sequence is converted to phoneme sequence and passed into the special gram prediction module as the input. After we have the synthesized special gram, we feed that as input to the waveform generation module to convert it back into audio signals. Three independent sequence-to-sequence network are trained in this framework. They are the prosody prediction module and the tackleshaw-based text-to-special gram model and the neural-vercoder wave-on-one respectively. The tackleshaw-based model is shown in B from the figure on the left and the prosody prediction model is shown in the right figure. The prosody prediction model here adopts three individual prosody networks for different prosody annotations in a theoretical structure to tag the prosody labels for the input sequence. For example, this is a sentence that has been added to the prosody labels where the symbol means prosody word annotation here. Then it is converted into a pin-in sequence and then the phoneme sequence. The final phoneme sequence is the input of our tackleshaw-based model. The synthetic result of our system is not as good as other teams this year. We rank in 14 out of 16 and we achieve a mouse-on naturalness with 3.2 and another 3.2 on speaker similarity. For the intelligibility, the pin-in error rate is 17.8%. And finally, the overall impression of our system scores 34 out of 16. Let's take a listen to some samples. The second one is for intelligibility test. The first one, same as the first one, is for naturalness and similarity evaluation. Here are the main references. Thanks for listening. I hope you enjoyed it. Please feel free to ask if you have any questions. Hello, everyone. Here is the team from South China University of Technology. Now we present our test-to-speech system for the BLEES search range 2020 health test. As so in the figure, our system used the tackleshaw-to-architecture to generate a form-level mouse spectrograph. And then we used the wave-inland recorder to restore the waveforms. In our front-end processing model, it contains a graphite-to-phoneme model and a bird-based bidirectional LSTN-CRF structure positive-dipodition model. Since the provided training dataset contains both Chinese sentences and some English words, we modified the graphite-to-phoneme model to support fighting ground test per-processing. And then we introduced a two-state training method to achieve fighting ground systems. In the first state, we used one English dataset and one external Mandarin dataset to fully change the tackleshaw model. Then in the second state, we fixed the parameter of the encode and only employed the provided dataset to fight the other part of the tackleshaw. Unfortunately, our system performed poorly. Our naturalness, MOS is only 2.6, and our pin-in error rate with tomes is 17.1%. Also, the voice similarity is written by the unclear output speech. We attribute our system to our two-state training method. For example, it ignored the difference of coherence between the characters in the dataset used in the two states. This is our lab of consideration in the sixth sense design, but I believe we will do better in the next time. And this is all of our presentation. Thanks for listening. Hello, everyone. I'm Xue Haozhou. I'm from National University of Singapore. Today, I will make a presentation here to present our paper for Blizzard Challenge 2020. The paper title is NUS HRT System for Blizzard Challenge 2020. My presentation will start from the introduction of TTS, then I will introduce our system architecture. What is text-to-speech? A text-to-speech system is able to generate the natural sound from the corresponding transcription. There are different techniques to build a TTS system. The traditional approach includes the parametric synthesis and unit selection. Recently, with the development of sequence-to-sequence network, the internal approach is a popular way to build a TTS system, such as Takochuan transformer TTS and fast-speech. This is our system architecture. Our system was built based on Takochuan 2. Takochuan 2 is a sequence-to-sequence, RNA-based network. However, we make some modifications on Takochuan 2. Our system input is phone. The phone identity is passed through an embedding layer to obtain the phone embedding. Similarly, the tone identity is passed through an embedding layer to obtain the tone embedding. We concatenated the phone embedding and tone embedding to form the new input to encoder. The relative skip connection was implemented in our encoder part. Additionally, we extracted the word embedding from a correction language model, and we combined the word embedding with encoder output to serve as the input to the attention-based decoder. The attention-based decoder predicts the male spectrogram from the encoder output. For Mandarin task, MH1, we used with RN as neural vocoder, because in this task, we had lots of samples to generate, and with RN is able to generate speech fast. For Shanghai NIST task SS1, we used with NET as neural vocoder. So, this is my presentation. Thank you. Thanks for your time. If you have any questions, we can discuss further. Thanks again. Hello everyone. This is the SOGO system for Blizzard Challenge 2020. We come from SOGO in Beijing, China. My name is Liu Kai. This is a flowchart of our system. It mostly contains an entry-end fast speech-based custom model and multi-band V-wire based on vocoder. To be clear in advance, we used some extra text and speech data for front-end and back-end models training. Now, I will introduce the details of the above system. These are the features of our system. In the text analysis section, we used rule-mod and ME model for text normalization, BSTM model for word segmentation, BERT-DM model for polyform prediction, and BERT-LSTM model for prosodic boundary prediction. We adopted fast speech as our custom model and made some improvements. Firstly, we used syntax-level VE to model the channel's different information in the training data. Following level VE is used to model the local prosodic information of sentences. In addition, we also used multi-band decoder and GAN to improve the local modeling accuracies of male spectrums. These are the predicted male spectrums for each system. It's clear that SYPS-D got the clearest results. We adopted V-wire as our base vocoder structure and 16-base sample, 32 kHz with MOL and PQMF subband, and we trained another textual line, a custom model for teacher-fast learning. Eventually, the speech quality gets a great promotion. Here are some demos. This is system A. And it's system D. The quality of system A to D greatly improved, and finally we submitted system D.  We used an improved fast speech-based custom model followed by a multi-band V-wire new vocoder and 32 kHz. It can generate very high-quality speech stability. In the future, we will make more attempts in reform modeling at higher sample rate and cross-liquid learning. Thanks. It's my honor to introduce the Royal Flash TTS system for Placer Challenge 2020. Fortunately, I will briefly introduce the tasks. There are two voices that need to be built from the released Mandarin and Chinese data, and our system is based on one-to-and-a-half-chon-two acoustic model followed by the RPCNet vocoder. This is the overall architecture of our system in training-fest, quarterly, we generated linguistic features, including phonium sequences and prosody boundary labels for Mandarin text using G2P and prosody annotation tools. RPCNet features are drafted as acoustic features when mass-stature grads are drafted as intermediate features. In census success, we adopted front-end to promote Mandarin text to linguistic features. Then they are played into the system and speech waveforms as generated. No external training data are used in our system. It is worth noting that we generate prosody boundary labels for Mandarin data while we direct Chinese phonium sequences and input of acoustic model. Different from original top-chon-two architecture, we further predict mass-stature grads, then CBHG module is used to transform them into the RPCNet features. With this approach, we aim to improve the stability of census success speech. We also apply GMM-based attention for long-form speech synthesis. 17 systems were evaluated for the half-test, including natural speech aid. As we can see, our system forms relatively well. The intelligibility test appears to still need to be improved in naturalness and similarity. 9 systems were evaluated for the 12 tests. Our system forms a moderate among all teams. So the royal flash details system has been introduced. In future work, more effective front-end module and high-platform Europe corridors has major changes over our system. Thank you. Hi, everyone. I'm so glad to be here to join the workshop and show the synthesis system for the Blader Challenge 2020 today. My name is Wendy He from the text-to-speech group of the Himalaya Company. Well, our presentation is consist of three parts, including the many architecture of our system, the overall results, and the conclusion in our future work. So firstly, in the graph, we will see the front-end module followed by an acoustic model. Finally, the male spectrograms will be transformed into the waveform by the vocoder. So let's focus on the front-end module. You know, in order to improve rhythm and pronunciation, we use a polyphonic disambiguation model and a prosthetic prediction model to correct the pin-in and generate a prosthetic break in memory data sets. As mentioned in the figure, the features of the data are extracted and then cognated to be the input of the BioSTM-based prosthetic prediction network. And after the output with the prosthetic label is generated, a BioSTM and CNN-based sequence-to-sequence model we proposed, combined with the rule-based G2-P2R kit are working for polyphonic disambiguation to generate the pin-in sequence with higher accuracy. So as for the acoustic model, Tecchrom 2 is also our best choice in this time and finally we got the waveform reconstructed by the WaveRNM vocoder. And it is worth mentioning that BEER has a 512-dimension random-initialized channel boss speaker embedding its implement for the fine-tuning the BLEED rendering data set from a pre-trained first speaker Tecchrom 2 model. And as for the evaluation results, our Himalaya TDSC system performs better and achieves higher quality in memory synthesis-dention-highness synthesis. So in conclusion, the final evaluation results in both tasks indicates that our system has a middle performance slightly above the average and we have much room for improvement in some aspect below and we hope to achieve better performance in the future. So that's all I want to say today. Thanks for your listening. Hello, everyone. It's my pleasure to share our project with you. We are come from Harbin Institution of Technology Shenzhen. My name is Fuhu Hao and my team member is Zhang Yibeng, Liu Kailong and Liu Chao. Next, we will introduce the TDS mode architecture from the following three parts. The first part is the front-end. First, we nominate the original text to more normal text and obtain the pin-in and linguistic future by open-source tools like JBA and PIP-in. And then we use the broad-based MSF model to predict the boundary break and it is more useful to make more natural speech. The second layer is sequence-to-sequence caustic model. First, we replace the encoder with a more powerful transformer network and introduce the local IR and to model the local information of the input text. And in order to enhance the robustness of the model, we also introduce the forward attention to replace the local sensitive attention in section two. And then we add the SSM laws with model training which can make the spectrum more sharp. And the third part is the encoder. Considering the speed of the encoder, we adopt the game-based model parallel with game. It's more faster than other encoders like with net, with IR net and LPC net. But the sound quality is a little poor. Finally, let's look at the result. Our system code is G. The result is not very satisfied. But our system has not of space, of the improvement both in front-end and the encoders. That's all for our report. Thank you for listening. Hello, everyone. My name is Long-Tong. It is my great honor to be here to give an introduction of our speech sensitive system for the Challenge 2020. I am a doctor candidate from the Institute of Automation, Chinese academic of science. My research interests in both speech-sensitive and voice emotion. And this is the main intent of my report. I would describe the CSIS speech-sensitive system about 9.5 hours of speech date for an user enter and 3 hours of speech from one native Shanghai eastern speaker I adopted as the training date for the construction this year. Our CSIS system is built based on the multi-speaker end-to-end model and the LPC net based on the neural recorder is adopted to improve the quality. The whole network consists of two components as shown in this feature. It is a typical multi-end-to-end speech-sensitive model which includes the technical and the LPC net. The speech database is about 9.5 hours of speech from one native Mandarin Chinese speaker. It contains about different speaking styles. We build a multi-speaker speech-sensitive system by adding external data. The composition of the external Mandarin database is shown in the above table. In the Shanghai list task we build a front-end system suitable for Shanghai list based on who we filming in this table. The filming labeling constructed by this method is more in line with the actual pronunciation rules of Shanghai list. The natural list and similarity were calculated. The results are shown in the above feature and our identity of the system is the same. For all this evaluation result our system only grants average level. So many works need to be done for us especially on improving the quality of the synthesized speech. Thanks for your listening. Hello everyone. My name is Hu Beibei. I'm going to present our system the Ajimide text-to-speech system for Blizzard Challenge 2020. Here is the overview. First, I will briefly introduce MH1 task. Then I will describe our TTS system. Finally, show the evaluation results. The MH1 task is to build a voice from a Mandarin Chinese data site which contains about 9.5 hours of speech data. We built our system for this task. Here is our system architecture. There are three components in the system. A bird based text front-end, a multi-speak text-to-model, and a Viva and vocoder. In the training phase we trained a bird-based G2P model on an internal polyphonic data site which includes 145 Chinese polyphonic characters. The TAG-22 model was trained from an internal multi-speaker data site and fan tuned on the MH1 data site. GTA was adopted to train the Viva and model. Our system ID is P. The right four figures shows the scores of our system. We achieved a good score on PTER. We believe it benefits from the bird-based G2P model. Besides, the Sanhe processing is also important. In the other task sections we achieved the middle scores. We will make more times to improve the system performance. Thanks for your listening.


Welcome to Toffy TV, it is the Everton News Daily. Everton are reportedly interested in signing Udenaisi centre back, Rodrigo Bacau, and that had cost him around £13 million, that is the fee being touted from sources in Italy today. Everton are reportedly interested in Bacau in the summer transfer window, and that interest hasn't gone away, apparently. Everton are thought to be prepared to let Michael Keane and Gerri Mina leave in the January transfer window, and whether or not they then see Bacau coming in to replace those two, if they went out, is another thing. Bacau has got 18 months left on his current deal with Udenaisi, Tuto Udenaisi reporting that again today with another Italian source as well. I don't know what to make of this one, like I said there was interest there apparently in the summer, he was mentioned quite a few times, and it's been mentioned against 26 years of age, I don't know I think Everton need to be looking at the other end of the pitch in my opinion, but obviously we've got, if you take Mina and Keane out of it, there's Holgate, Goffrey, Tarkovsky, Conacody, Rish Welsh, Branthwaite, they were all still there, it's the one area that Everton are really well stocked, so who knows what to make of this one, but it has been reported today and that's why you are getting it here on the news. Everton have also again today been linked with a move for Daiji Kamade, who is having a really good season with Iron Track Frankfurt, the 26 year old out of contract next summer, Everton very interested in taking them, there's a lot of other clubs like the look of them as well, he's having a brilliant season with Frankfurt, he's got seven goals and three assists in the Bundesliga for them in 13 games, he scored three goals in six Champions League games as they got through in the end and in the German Cup he's played two games and scored two goals, so he sounds exactly what Everton need, given Everton have scored 11 goals this season in 15 Premier League games, they need a player who can score goals again because he's had a contract in the summer, if he wouldn't be huge, he's an attacking midfield player but he's also can operate in a wide area, not necessarily as a winger but as a wide forward, I'm sure there will be lots of interest in them as well but Everton again credited with having a big interest in taking the player in the summer, it's thought the player is interested in coming to the Premier League gives Everton an opportunity but this is the kind of player, even if people might think this is unrealistic, I don't know, but it's the kind of player Everton do need to move for, we need a player who can put the ball in the net but also a creative player just looking there with the seven goals and three assists, ten goal involvement just in Bundesliga games alone this season 13 games, so it's exactly what Everton need, I don't know whether they could get them but it's exactly what we need and these are the kind of players we do need to be chasing. It's well documented how poor we've been in that final third this season, so let's hope there is something in that. Moving away from that it is obviously the eve of the World Cup, Franck Lampard has been speaking about Conacody and has likened them to having the same qualities as what John Terry is obviously his captain wasn't he, it's Chelsea, former England centre back as well and this is what Conacody had to say about those comments. It's a privilege, it's a moment when you hear things like that, it fills you with pride and it's going to put a smile on your face no matter what you look at, I play under a fantastic manager ever and he's fantastic, everybody helps everybody, he speaks to everybody, he's a huge manager and we all know what he was as a player as well so what he's done for me and how he's helped me has been brilliant but to hear them weird honestly it fills you with pride because like I said it can't not put a smile on your face. There you go, it was obviously John Terry was a fantastic defender and you know a leader and all that so I'm sure Cody pleased to be linked with him there, Cody was given the number 16 sheet number for the World Cup and sent out to face the press, there you go, make it that what you will but he is a good talker isn't he and a bit of a leader so that's probably why England have given him the press conference today. Cody was also asked about the form of Jordan Pickford and what it's like, what kind of character he is, what it's like to play with Jordan Pickford every week, this is what the Evan Defender had to say about the England number one. He's a top lad and he brings so much, we see him as a shot stopper but as a person he's so so good to have a round as well and he's been England number one for a while now and I think he's been absolutely outstanding this season, I really do, I've been looking off to play in front of him and what he brings to us at Everton but even beforehand when I played for England as well I knew the quality that he had and I knew what sort of character he was as well but now I get a chance to experience it day to day and like I said he's a great friend of mine and a great person as well yeah. There you go, praise it for Jordan Pickford from Connor Cody, England kick off their well cup campaign at one o'clock on Monday, you'll be able to see whether, well see how Jordan Pickford does and whether Cody is involved, we're having a Englander round watch along on YouTube on Monday so join us if you've got the time, why not join us and we'll watch it all together, there you go. And finally Alex O'Woby is in the Nigeria start on 11 tonight, they're playing against Portugal in a warm up game for Portugal and Seamus Coleman should feature for Ireland tonight, they play Norway so it'll be interesting to see how Seamus copes with Ireland if Harlan plays so there you go. There you go, that is it for the news daily, today give the video a thumbs up, subscribe if you haven't, if you want more videos including exclusive videos, I've got a couple of them coming up over the weekend that you won't want to miss, then the link is in the description and the QR code is on the screen now to join Premier, see you later.


Hi everyone, welcome back to my channel and welcome back to my kitchen. So we are going to tackle a weekly meal prep today and I am trying to get back on the train of doing this. It always makes our weeks go so much smoother whenever I can prep everything as much as possible. So for Monday, we are going to do a Mary meat chicken with Parmesan potato bites and green beans. And then I also am doing Parmesan radish bites as well along with the potato bites, which I will be showing you here in a minute. The first thing that I'm doing is just making sure that the potatoes are all washed up. And I'm just using these small red potatoes. I know that they go on sale at different times at my grocery store. So I like to grab them whenever they're on sale and they make a great smash potato, which is a favorite of my families. This Mary meat chicken recipe is really, really simple. So the first thing that I'm doing is just trimming up the chicken breasts. And there were one or two of them that were a bit on the thick side. So I went ahead and cut them in half. And then I seasoned them with some salt and pepper.  So while the frying pan was heating up, I went ahead and washed up these beautiful radishes. I always think that radishes look so pretty and they remind me a little bit of the Peter Cottontail story books for some reason. So what I'm doing is just trimming them off of their leaves. And if you have any ideas on how to use radish greens, I would love to hear in the comments, especially if you are from another country and it's a way that you make use of those. Let me know. I'd love to know just for the sake of when I buy radishes like this or whenever I get radishes out of the garden. So what I'm doing is just trimming the ends off of these and it helps them to smash out a little bit more like the potatoes. So if you're someone like me that's watching their sugar intake, the radishes is a great alternative and really tastes delicious. My husband who is kind of a picky eater, actually really enjoyed the radishes just as much as the potatoes. So definitely give them a try. And if you don't like the flavor of radishes, these don't really have a radish taste. Since you boil the radishes first before you smash them, it really kind of mutes their bitter or hot taste that they can have. So to go along with that, I shredded some Parmesan cheese and we're gonna need that for both the smashed potatoes and radishes and the Mary Me chicken. So I think it's about one third cup for the Mary Me chicken. Don't forget that the recipes will either be typed out in the description box or linked below if they're from another recipe. And then I also smashed up some garlic using my rocking garlic smasher. I really like this thing. It's just easy to clean compared to a lot of other garlic mincers. And I really like using this to even mince other things as well. So now that my oil is hot in the pan and I used avocado oil to fry these up, I went ahead and laid about half of the chicken breast pieces in there. And remember we had seasoned them with salt and pepper so they just need to be fried up and cooked really well before we start making the sauce that goes on the Mary Me chicken. So here the potatoes are done being cooked so I could put a fork through them and I just drained them off and now you're gonna see me take a heavy glass or a heavy bottom glass and just smash them out. This is really, really simple and you can actually do like cheddar cheese on top of these as well. But I love the flavor of Parmesan cheese and Parmesan cheese can be one of those cheeses since it's so aged that is a little easier on your tummy compared to younger types of cheeses. And then here you're seeing the radishes and you can see once they've been boiled how much color is taken out of them but they are still delicious and kind of give you that potato texture instead of having the right real potato. So I'm just taking some pink Himalayan salt and sprinkling that over top of the radishes and potatoes and I should have gotten my small oil dispenser out to squirt over this because as I was trying to drizzle the avocado oil on these it just blow a lot more dumped out than I expected. So they got a little extra oil but that's okay they still turned out delicious. Once I had the oil on then I sprinkled them with the parm and popped them in the oven and I put them in the oven at about 400 degrees and I think they were in there for maybe 20 minutes or so 20 to 30 minutes depending on how melted you want that cheese to be and you can actually put it under the broiler which is what I did at the end just to make the cheese brown up a bit and then I took some of my home canned green beans I put them into a pot on the stove with some butter and all you gotta do is heat them up. This summer I plan to show you all how I can green beans I used to freeze my green beans but last year for the first time I canned them and I'm not going back I love having them canned you can heat them up so quickly. So here you see I took the chicken out of the pan I put that minced garlic right into the pan and then I put some of my home canned chicken broth which I'm running low on so I expect a video soon showing you how to make chicken broth and beef broth because I'm out of beef broth and I need to make chicken broth soon so I will be showing you that and then here I used these smoked sun dried tomatoes these are so delicious and have a wonderful flavor and then I put some heavy cream if you want to make this more so dairy free I realized that it has Parmesan cheese in it but like I said some people can eat Parmesan cheese that are mostly dairy free you could use canned coconut cream instead of the heavy cream so here I'm adding in some oregano and once this hit the pan a few minutes later my daughter walked out and said something smells like pizza so this definitely has a bit of that Italian flair to it so once the sauce had cooked up for a bit I went ahead and put the chicken breast back in and I put the lid on my skillet and I let this sit for about 10 minutes or so just really letting the flavors combine and here you're seeing all of the components of the dinner together and it was delicious so for Tuesday we are doing taco salad with a cilantro lime dressing so I put about two pounds of ground beef into my large frying pan to fry up and then I also got out my salad spinner and I had gotten this organic romaine lettuce and I'm just cutting that up I'm trying to get away from using as much iceberg lettuce just because I know there is almost no nutritional value in that I don't know if romaine is any better it's a little greener so that's my thought process but I'm going to try to use this for our taco salads we usually would have used the iceberg lettuce so I love this salad spinner you all know you've seen me use it a lot of times and I just love using it for berries and for many other things and it's got this little button right on the top that is a break for it and it makes it stop spinning when you want it to stop to store this lettuce since it was going to go into my meal prep section of my refrigerator I'm just putting a piece of paper towel in the bag with it to help absorb any extra moisture great so now I'm just preparing some of our toppings for the taco salad I'm cutting up some green onion I love green onion I think I say that in almost every single meal prep because I find some place to put it but I love the flavor of it and I like the color that it brings to dishes and it's kind of an inexpensive budget-friendly piece of your meal and doesn't cost a whole lot I go on with that I cut up some lime wedges for us to squeeze over our taco salads and then I just cut some cherry tomatoes into fourths put them into a container and as always I love to shred my own cheese so I went ahead and just shredded this up and I've been doing a lot of minimizing in our home if you all watch my home channel you're gonna be hearing a lot more about just learning to live a bit more minimal than what I have and so I'm debating on getting rid of my food processor I do use it however I don't mind box grading cheese either like I am here so I don't know let me know in the comments if you have a food processor do you feel like it's worth keeping or do you think that it's just something that takes up extra space in your cabinets? Okay, you all know me I'm a healthy thinker I like to think about healthy food and I don't really like using taco seasoning packets however, this is one area that I compromise because my husband loves whenever I use a taco seasoning packet he always feels like it tastes so much better than my homemade taco blend I don't know if it really does or if it's just the thought that it is there so I went ahead and used that but I do try to buy the ones that have a bit less sodium here I am mixing up this dressing again, the link will be below it's really simple and this is something where I didn't really measure I did kind of some guesstimating but it just takes some mayo some sour cream here we go, the Worcester Shire Sauce if you guys have been around for a while you always know I struggle to say that in fact, I get funny little memes you guys send me on Instagram about saying that word and everybody always puts their breakdown in the comments of how to say it but either way, I still struggle and then it just takes some lime and a few other seasonings and some salt and then you're gonna cut up some fresh cilantro to put in with it and I was really glad we didn't eat this right away because I felt like the cilantro sitting in the dressing in the refrigerator for a day or two just helped the flavors to combine and my husband really liked this dressing as well so if you want an easy dressing of course it's not totally dairy-free but you could use a dairy-free sour cream in place of the regular sour cream and still be able to make it meals like this one that take a lot of different components are so incredibly handy to have prepped it was so nice on Tuesday when we pulled this taco salad out I could just heat the meat up and we could build our taco salads everything was ready to go nobody had to wait for dinner and it so happened that that day we needed dinner kinda quickly so it worked out really really well okay so now for Wednesday we are going to be doing a broccoli cheddar soup with turkey melts so to start out I'm just cutting up some broccoli florets and this broccoli soup was so easy I've made quite a few different versions of it and this one was really really simple so I wanted to give it a try I think it only has a handful of ingredients and it's just very easy to put together so you could definitely use frozen broccoli and I think that often times you can get frozen broccoli for a good price if it goes on sale I know there's times where you can maybe get it for a dollar a bag so potentially this soup could be very inexpensive and I don't know if it's because I grew up with broccoli soup my mom made broccoli soup quite often when we were younger but I just love it it's such a comfort food for me and with this one having such simple ingredients there is no flour added to this so it is gluten free it just feels like a healthy good meal and since I'm using my homemade chicken broth in this it is more of a bone broth and there's a lot of health benefits to that as well so here I am shredding up the cheddar cheese for this as well and I just feel like till I washed my food processor I might as well get out my box grater and it's so much easier to clean up so I don't know let me know again if you guys think that I should get rid of my food processor and here I'm just adding some of the minced garlic to the bottom of the pan with a little bit of avocado oil here's my homemade chicken broth and I love this now that I've made it myself and have used it for a while it just seems like store bought broth can't compare because I know all the veggies I used in it and the flavor is just so delicious every time I make a soup with it we all just feel like it has such good bold flavor so here I'm actually crossing over I'm beginning Thursday's meal prep and Thursday is going to be quesadilla burgers with a cauliflower salad and I'm kind of combining two meals or two days here because it was just good for my time and since I was waiting for that broccoli to cook up for Wednesday's meal I decided to go ahead and mix up my burgers so if you guys have been to Applebee's you know they have a quesadilla burger and it's one of my husband's favorites he really likes it so I decided this week I am going to figure out how to make these myself I found a great recipe on Pinterest I will leave it linked below and I didn't follow the instructions to a tee here I'm just adding a lot of southwest spices southwest inspired spices to the burger I cut some parchment paper just to put between the burgers so I could store them in the refrigerator I also felt like this is a good idea if you are going to really add some spices and flavor to your burger patties is to let them sit in the refrigerator for a day or two it will really help the flavors to combine and a lot of times you know we mix up the burger patties and we make them right away but I know there's times that we've bought pre-made patties in the meat section at the grocery store and we always think their flavors are so good and I think some of it is because the patties have been made and they've had time to sit and kind of marinate in those spices so that's kind of the idea that I have here and then I just am showing you the small flour tacos that we are going to use whenever we make these we'll add in avocado we'll add in salsa and just make them very southwest quesadilla-y and then melt them together with the burger inside of those tortillas to get that kind of crunch on the outside and there you have a great quesadilla burger so here we are back at the broccoli soup and you saw me add in that cream again, if you wanted to cut down on the dairy or if you wanna make it dairy-free you could use coconut cream and you could use dairy-free like cheddar cheese or fake cheddar cheese to make this soup and that's how easy that soup was I just put the immersion blender in a little bit and I put it into some containers put it in the refrigerator and then we just reheated it when we were ready to make that and then I just made some turkey melts with it where we do some roasted red peppers, avocado, turkey and some cheese in bread and melt it together on the stove and that and the broccoli soup are just delicious so here I am making this cauliflower salad and I really enjoy egg salad we like deviled eggs in our house so I thought we probably would really like this so you just cook up or steam up some cauliflower florets and then you wanna take and separate the whites and the yolks of the hard-boiled eggs and you make kind of the sauce with the egg yolks so I added some mayo, some dill relish and some yellow mustard and the mayo I've been using a lot lately has been Primal Kitchen's avocado mayo it's just got better oils in it and you're not consuming so much vegetable oil just a little kind of review there that we've been really liking that and then you add in some paprika I like to use the smoked paprika and as you're seeing here I'm also adding in some green onion and just smashing it all together the recipe is below for this so once you have your kind of sauce made up you're gonna put that in with the cauliflower and the egg whites, stir it all together and there you have a cauliflower salad and I think that this too is going to be a lot more flavorful from getting a good day of sitting in the refrigerator. All right, so Friday's meal I'm not really prepping just because it's a handful of days away and it'll be easier for us to do on that day so I'm going to be doing some chicken thighs and we actually may put these on our smoker if I don't do them on the smoker then I will do them in the oven I will leave a video link below for the last time I did that to get the skins good and crispy and really make a delicious chicken thigh and what we like to use with them is my homemade seasoned salt this is a bit like the loury seasoned salt but I do a version that does not have sugar in it most all of the seasoned salts have sugar in them and that's what makes them good but I use an alternative I will leave the recipe for this in the description box below typed out so if you want to copy it and paste it to your notes or take a screenshot of it you can do that and then to go along with this I may do some sort of drop biscuit I will leave a recipe below for one that I like and then with that I'm going to do some stuffed little sweet peppers I'll just use some bacon bits some cheddar cheese and cream cheese cut these in half, stuff them and pop them into the oven at 400 degrees for probably, I don't know, 15 minutes or so I'll check them and see how soft they are and we'll have some poppers that aren't hot I know a lot of times people make jalapeno poppers but these will be a sweet version so that our kids will like them as well so I just got these out of the freezer so this is your little memo if you are making things this week that you need to grab things out of the freezer go ahead and do that so you don't forget and then you will end up with a throw together meal instead of something that is planned out which is the ideal situation with meal prepping Thank you guys so much for watching today I hope this video inspired you I hope that you get ideas for your week this week let me know in the comments any good meal ideas you have, any meal inspiration I'm always looking for more ideas and don't forget to subscribe if you're new, give this video a like put that bell notification on so you don't miss any videos that I have coming up I'm going to be posting a lot more frequently and I will see you guys in my next video


Here's an interesting question. How long should a run be? Like how long should you run for?  How long should I run for if I'm training for 10k? What if I'm training for a marathon? How long should you go running for? Long to go running. It's a pretty difficult question. It's kind of an impossible question to answer. In a sense it's kind of like asking how heavy is a rock? I mean there are all kinds of sizes and weights and you can run for short times and long times. You can run hard, you can run easy. That all matters you know. But in general let's just start at the beginning.  How long should you go running for?  I would just go for a walk three times a week and just get that habit in. Let's say during the middle of the run, sorry the middle of the walk, you just start running and you run for like as much as you feel comfortable with. Say it's maybe it's five minutes. Maybe it's one minute. Maybe you can go 10 minutes. But that's probably enough. Then you stop running and you keep walking and you finish your walk and you do that for a week or two. Then eventually you'll be able to build up how long you're running for each time and eventually you're out there going for a run. That's typically how beginners start running. If we're talking from a physiological standpoint like training, how much should you run in order to get a training benefit, in order to stimulate the development of your aerobic system, of your heart, of your essentially build fitness. Well you know anything more than you're used to will typically stimulate in effect. But let's just put an number to it anyway. I would say 20 minutes. 20 minutes is a good minimum amount of running. Once you're through the beginner phase of walking and running, if you can go for a 20 minute run, and this is actually true for even top level athletes, a 20 minute run will have some benefit to it. It's enough to get the system going. You know, it's typically the time it takes to warm up, right? So basically that means it's the time it takes to get everything flowing and things happening. After 20 minutes, if you stop your run, after 20 minutes, suppose you do that three times a week, that's an hour per week, that's pretty good. That is going to give you some bang for your buck. Of course, from then on up, it's just almost the more the better, unless you, as long as you don't get injured. So if you go for 30 minutes, it's probably better than 20 minutes, 45 minutes, even better. Going for an hour, that's awesome. A lot of things, good things happen when you go running for an hour. But you've got to remember also that if you had a hard workout yesterday, perhaps today you need a rest day. Or if you, if you can tolerate some running, perhaps today 20 minutes would be perfect. That's what a lot of elite athletes do. You know, it's all about polarizing your training. And as a running coach, when I make a training program, I try to polarize the training so that we have, we make the hard days harder and the easy days easier, which can also mean making some days longer and bigger with more volume, more time running and other days smaller with less time running. So suppose I had a two and a half hour run yesterday, I might just take a rest day today. Or if I'm going running, perhaps I'll just do it for 20 minutes, half an hour is just to get some blood flow and to get a little bit of a stimulus, but while still recovering from your day before. So in the upper end of the spectrum, how long should a run be? Well, ultramarathon runners, they might go train and do trainings run that training runs that last for like six hours, but that's pretty extreme. Even a four hour run could be beneficial for an ultramarathon runner. If you're talking marathon training, typically most marathoners are, you know, better off just running two hours to perhaps three hours for their long run. That means once a week, the other days of the week, they'll probably just do 45 minutes, an hour, an hour 15, perhaps even one and a half hour, 90 minutes. But you know, the thing is at the high level and at a low level, it's all about that cumulative effect of steadily chipping away at it day after day. Anyone can handle or not anyone, but it's easy to handle a day of, you know, a couple of hours of running. But what if you're next, the next day as well, you need to do it and again and again and again over time accumulating a lot of time running, that's what's stimulating the best effect. So how long should you run? It depends on how much time you have. It depends on how fast or how much you would like to improve. It depends on what you've done before. It depends on your current fitness. There are so many factors and variables that makes it very difficult to answer. But I think I've answered it for the beginners, you know, start walking, doing some running, eventually you get to 20 minutes. That's a decent amount of time to run. And as I said, most people, even the higher level athletes, run between half an hour and three hours for most of their runs. And even more so, 45 minutes to one and a half hour, that probably covers 80% of the runs that most people do. So there you have it. The idea here is that after you go beyond say two hours, two and a half hours, there's the principle of diminishing returns kick in, which means that you can go longer, but the benefit that you get from it in terms of stimulating adaptations, getting fitter, are probably not worth the extra risk of getting injured. So running more than three hours, there's a high injury risk obviously. But the benefits you get from running three hours versus running two hours, maybe it's not that great. Maybe it's better to just do that two hour run and then come back tomorrow and do another run of say one hour and you cover three hours in two days instead of one. That's a little bit better. And intensity plays a role here as well. Of course, if you're running hard, you cannot run for as long as if you're running easy. So you've got to balance intensity and volume. Yeah, I think that's a good answer. How long should run be? If you have any questions, of course, you can always contact me at the long trail Facebook page, or you can contact me on my website, MGD coaching, there's a link in the description. I offer customized training plans and coaching calls via Skype. So feel free to contact me there and of course, subscribe and stay tuned for more videos. Thanks for watching.


Freedom, let's talk about Trolls. Trolls are those people online whose entire existence is defined by getting a reaction out of you and bringing you down. Oh my god, do we know that? Why is he telling us this? I'm bringing this up because Trolls are a huge part of the YouTube ecosystem, and it's important to understand how to utilize Trolls as a way to grow. That kind of sounds strange when you put it that way, doesn't it? Trolls don't help, we're only here to insult your mom. In this video, I'm going to provide a few relevant examples of trolling, and I'm going to use this channel as an example. What do you mean? I don't troll this channel, all of my input is valid. Before we get into all that, I need to ask you a very valid question. At what point is someone trolling? Many times people will leave a comment, and because we all say this in our heads, we have context that doesn't really translate to text. So for instance, if I were to say that your video quality is bad, of course, I may know in my head exactly where I'm going with that, but if I'm not communicating that properly, that can be a troll comment. A lot of times, something that you may take to be a troll comment can actually be someone trying to help you, but maybe not understanding how it's coming across. Engaging these comments and asking for more information often is the best way to handle this, because you never know who's actually trying to help and who's trolling. Starting conversations with these people can help you kind of get the ball rolling on some conversations, and who knows, you could end up making a friend. Why don't you ask your mom what I mean? Like she totally knows because I told her last night. Other times people are just not believers in your brand. For instance, a lot of people question George's authenticity because of how happy he is in his videos, how over the top and just gushing he is. Let me share something with you about George. George is the same happy, optimistic person in person that he is on his videos, and this is just who George is. Now, of course, maybe he on his videos dials it from a 10, maybe an 11, but he is in fact that person. It might be kind of hard for a lot of people to believe, but that's just who he is, and that's the kind of content he enjoys creating. Happy people can't be trusted. They make medicine for that. So when you have someone on your channel who you're not really sure if they're meaning well and maybe just coming off, I don't know, maybe a little trolling, or if they are indeed trolling, the best idea is to treat this person like they are your biggest fan, or that they could be your biggest fan tomorrow. You never know what relationships will develop out of YouTube comments. Some of my best friends to date were at one point fans, or at least people who found my content. What do you mean? You only have like five subscribers. You don't have any fans?  Everyone has people that enjoy what they do, and many times when people don't realize that they were maybe unintentionally trolling or that their comments might have maybe been taken wrong, or that their inner dialogue gave some kind of context that was missing in the text post, a lot of people will change their tune, or they will bring themselves out into the light as nothing more than a troll. One of the things that I absolutely love about this community is the fact that even the trolls are the nicest possible people once you get to know them. Don't call me nice. I don't want to be nice. I want to be like awesome and epic. Never, ever, ever become confrontational with one of these people. That's not gonna help you at all. In fact, a lot of times what you're going to end up doing is playing directly into their hands. A lot of people will use this against you later, or they will use this as a justification for why they didn't like you in the first place. Often, just being happy, being friendly, and being helpful is the best possible solution. Just like if you worked for a multi-million dollar corporation and your job was on the line, that's how you want to protect your name here on YouTube. What are you talking about? You only have like five subscribers. That's like not worth protecting. It's not really a brand anyway. Anytime you pour your heart and soul into something, or attach your name to something, that is your brand. That is something that you are working to complete as a whole, and that's something worth protecting. It can be anything from your channel art to your company logo. This can also be the way that you interact in your videos, how you begin and end them. This can also just be how you interact with your community.  I am getting so bored right now. Tell me something I don't know. All right. Well, how about this? Trolling is nothing new. Trolling has been going on since anybody ever tried to do anything. Trolling is a byproduct of a few different things. Now, trolling among friends is usually lighthearted and really only a joke. However, online usually it's not necessarily lighthearted and it's really not a joke because it does hurt people's feelings. In the old days, let's say back in the 70s, 80s, 90s, further back than that, maybe these people were called hecklers and they would show up to comedy shows and plays and things like that. And they would ruin the entire show. Going back even further than that, these are the people who would throw rotten tomatoes at court jesters in the King's Palace. Are you trying to say that YouTubers are like court jesters? Like they don't even wear the same silly hat. Recently on this channel, Keemstar put out a video reminding everyone that entertainment quality is in fact very important. In fact, it's one of the most important things. I spend a lot of time talking about the metadata, the tags, title, description, optimizing that way. However, if your content's just not there, well then there's no amount of tags, title, description, or sharing it that's really going to help at all. And a lot of trolls kind of latch onto this or let's face it, a lot of them have absolutely no clue what goes into really making a video like the one you just produced. What's the matter, you don't have anything there? Uh, no, but I have a cousin who's like over famous, but you can't meet her right now because he's in the Swiss house with his wife on vacation and she's a model. It's also vitally important that you understand that engaging these trolls is really the key to helping yourself grow as a creator. Because through some of this that may be taken as trolling, you can actually grow your quality, grow your brand, your subscribers, your views, all of these things that we use as metrics for growth. I'll use an example from this channel. For instance, there were some people who at first I thought were trolling about the quality of the videos I was putting up.  Explain what you mean. And through a few different conversations, I got the quality of these videos higher and higher and higher. It's still not perfect, but it's something I'm working on. And if Adobe Premiere would stop crashing, I would really be nice. Is that why you had to steal a bit from Ray William Johnson with his troll? Also, any videos that require any sort of planning, if you just take a little bit of time to plan ahead and get to know your audience, get to know your trolls, you can actually maybe put in a couple of Easter eggs or throw in a few comments to combat trolling before they even happen. This is usually more of an advanced technique. And it's something you should definitely do tongue in cheek. But if you address an issue or a quality concern before they do, well, it's kind of like making fun of yourself before the bullies do. It really does work. And it takes a certain amount of sense of humor to really pull off and to do well. I would tell you to be more creative, except for the only thing creative about this video is the fact that your mouse is manufactured by creative. Just like in the business world, you have to keep in mind you have an image to protect. So you can't really engage these trolls in a negative way. Staying positive, asking questions and interacting as if this possible troll is your biggest fan is going to be your best source of gaining new fans, even if that troll does not respond well. Other people who see it in the comments will eventually look at it and go, Hey, this is a nice guy. He's really trying. And if that doesn't work, asking loaded questions might also be an okay idea. Are you trying to pantomime like you're typing or are you just trying to tickle me? I hope that this video has been helpful for you. If it has, let us know in the comment section down below. My question to you is what do you do when you have a troll on your channel? Has this video made any difference whatsoever? And I think that this is going to be the only time I will ever do this ever. But freedom, I want you to give me your trolliest comment. Go ahead, let me have it. Let me know if you were a troll, how you would try to get on my nerves. And in the comments down below, I'm going to try to see if I can tell the trolls from the non-trolls. This might be fun or I might regret it. Till next time, I'm Anthony with freedom. Like, subscribe, comments please. And we will grow together as a family because this is the freedom family. You are part of it, we are all part of it, and we're all growing together. So grow with us and partner with freedom. That play now button right there on the video bar is all you need to click to get started. In this video, I'm going to provide a few relevant examples from this channel, and I forgot the rest of the line.


When I was 16, I wrote a letter to my step-grandpa and I said, Life's too complicated. You've got too much you have to figure out your political party, who you're going to marry, what you're going to study, what your career, how you make money. I said, well, you give me all the answers and you wrote me back and it said, Ty, it's too complicated. If you're lucky, you'll find a few people who will mentor you. And that's what I did when I traveled around the world. Pablo Picasso said, good art is coffee, great art is steel. So if you can find somebody that you can shadow, that's what I started doing.  You can make a million dollars an hour.


Hi everyone, Joe for jasmizcasewrakes.com with a random number block randomizer for just these two teams right here, France and England. That's how it all works there. Big thanks to this group here. Thanks to the numbers right over there. Let's do it. Let's roll it. Randomize it three into three. Six the hard way for names and numbers. One, two, three, four, five and six. Jeff down to Alex after six. And then six times six the hard way for the numbers. Two, three, four, five and six. Seven down at two. All right, Jeff with seven. Alex with four. Brian with nine. Michael with three. Andrew with eight. Michael with five. Jeff with one. Andrew with zero and six. And Alex with two. Let's order these numerically. And coming up in the next video will be that one box break itself. We'll see you for that jasmizcasewrakes.com.