Learning the meaning

Technology QuarterlyDec 8th 2001 edition

TO GET a machine to recognise speech, the first thing you have to do is to capture and digitise the actual sound. This is then manipulated mathematically so as to reduce “noise” and other stray signals that have crept in. Next, the cleaned-up stream of digital sound is chopped into pieces. These are matched to words, or bits of words, using statistical algorithms that rely on knowledge of how language is spoken.

But it is not enough merely to recognise the words. If speech recognition is to be effective, the computer must literally understand those words and be able to use them in the right context. Therefore, before doing anything else, it has to “learn” the meaning of thousands of sentences. It does this by breaking down sample sentences into their grammatical components, and being told what each of those components means. So, when it has a component such as a word or bit of a word (eg, the “ziz” sound of the possessive “s”), it can retrieve the appropriate meaning.

The leading companies in this field include SpeechWorks International of Boston, Nuance of Menlo Park, Philips Speech Processing of Eindhoven, and Lucent Speech Solutions of Murray Hill.

The ability to generate realistic speech from text stored in a computer will be crucial if voice-enabled services are to take off. A firm that wanted to provide customers with a voice-enabled ordering system for its entire catalogue could easily find that the inventory was far too long, and changed too often, to be recorded by human actors. This is where tricks learned by AT&T, with its advanced text-to-speech technology called “Natural Voices”, come into play.

This works by first recording various dialogues read by a human, and then transcribing this information into sounds. The text and the sounds created are catalogued in a database. When queried by a new piece of text, the database retrieves the appropriate bits of speech and assembles them in the right order.

In technical fields, it is possible to create specialised databases that improve the accuracy of the spoken responses considerably. The speech that systems such as “Natural Voices” generate is exceptionally convincing. Engineers working in the field expect to be creating computer-generated speech that is indistinguishable from human speech within a year.

Researchers at AT&T are also working on so-called “voice fonts”—ie, different styles of voice and accents. This raises the interesting possibility of designing “voice brands” associated with specific companies. Alternatively, the voices of well-known personalities could be captured in a recording studio, and then used in games and cartoons, or to endorse products, with the person never having to step into a recording studio again. Another possibility is to capture and sell “voice-prints” of celebrities in much the same way as their photographs are marketed.

This article appeared in the Technology Quarterly section of the print edition under the headline "Learning the meaning"

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