Text-to-Speech for low resource languages (episode 3): But can it say �Google�?

Text-to-Speech for low resource languages (episode 3): But can it say �Google�?
Di Posting Oleh : NAMA BLOG ANDA (NAMA ANDA)
Kategori : Machine Learning TTS

.DialogCon { text-align: center; color: rgb(102, 102, 102); width: 825px; background-color: rgb(255, 255, 255); } .xDialog { position: fixed; z-index: 1000; left: 262px; top: 300px;opacity:0;} @media screen and (max-width: 600px) { .DialogCon { width:300px; height: 120px; } .xDialog { width:300px; height: 120px; left:0px;} } .DialogCon2 { text-align: center; color: rgb(102, 102, 102); width: 825px; background-color: rgb(255, 255, 255); } .xDialog2 { position: fixed; z-index: 1000; left: 262px; top: 300px;opacity:0;} @media screen and (max-width: 600px) { .DialogCon2 { width:300px; height: 120px; } .xDialog2 { width:300px; height: 120px; left:0px;} } .DialogCon3 { text-align: center; color: rgb(102, 102, 102); width: 825px; background-color: rgb(255, 255, 255); } .xDialog3 { position: fixed; z-index: 1000; left: 262px; top: 300px;opacity:0;} @media screen and (max-width: 600px) { .DialogCon3 { width:300px; height: 120px; } .xDialog3 { width:300px; height: 120px; left:0px;} } Posted by Martin Jansche, Software Engineer, Google Research for Low Resource Languages

This is the third episode in the series of posts reporting on the work we are doing to build text-to-speech (TTS) systems for low resource languages. In the first episode, we described the crowdsourced acoustic data collection effort for Project Unison. In the second episode, we described how we built parametric voices based on that data. In this episode, we look at how we are compiling a pronunciation lexicon for a TTS system.

In Project Unison we are developing ways to bring Google's spoken language technology to the world�s major languages. As part of this broader goal, we are piloting a process for building a text-to-speech (TTS) system that can speak Bengali (Bangla). While our exploration of new methods have allowed us to gather sufficient data to train a statistical parametric voice capable of speaking Bengali, we had to address the next challenge: How do we make the voice sound like it is fluent in that language?

When people learn foreign languages, they are usually expected to pick up the full details from repeated exposure once they've mastered the basics and reached sufficient fluency. Often second-language learners struggle with issues that may seem so natural to fluent speakers that they are taken for granted. For instance, in order to read text out loud, one must know how to read different kinds of numerical expressions (e.g. dates, times, phone numbers, Roman numerals), and how to pronounce a wide variety of words, ranging from native words to newly coined brand names to loanwords, which themselves can arrive from different source languages. As TTS systems heavily rely on machine learning, they tend to face similar challenges as human learners: the way words are pronounced is often complex, sometimes surprising, and rarely fully documented.

Take the Bengali word meaning "microscope", which is ?????????. Its pronunciation can be transcribed in the International Phonetic Alphabet as /o.nu.bik.k??n/. When our system encounters this word, it analyzes the spelling in Bengali script into abstract written units called graphemes and then predicts the spoken sounds, or phonemes, of Bengali phonology from these graphemes.

The correspondence between graphemes and phonemes varies along several dimensions. One dimension is horizontal complexity: in many cases a single grapheme corresponds to a single phoneme, but the Bengali ligature ??? is special, as several graphemes correspond to several phonemes in a somewhat surprising way. Another dimension is vertical predictability: a grapheme may correspond to different phonemes in different contexts and the correct phoneme may be difficult to predict. The Bengali grapheme �a� / �-a� is both very frequent and its pronunciation very unpredictable. It either corresponds to the phoneme /o/ or to the phoneme /?/ or it is not pronounced. In the table above, we see all three possibilities within one word. As is standard in speech processing, our approach relies on human experts who transcribe words into phoneme sequences and machine learning models that capture the complex aspect of the grapheme-phoneme correspondence.

In order for our Bengali TTS system to pronounce the words in a sentence, it relies on a pronunciation dictionary, or lexicon, that provides pronunciations of a number of common words. When a word is not in the lexicon, it falls back on a machine learning model that was trained on thousands of pronunciations, which can then provide a pretty good guess at how a previously unseen word is pronounced. With a sufficiently large pronunciation dictionary, the system can be expected to reach a high level of fluency.

We first started compiling a Bengali pronunciation lexicon, with our Bangladeshi linguists transcribing a few thousand words into phonemes. This work was done in a web application that had been custom built for this purpose. Just like an earlier version, this transcription tool supports the work of linguists by providing a virtual keyboard for entering phonemes.

Once a few thousand words had been transcribed, we trained a machine learning system that could predict phonemic transcriptions for previously unseen words, so that the linguists only had to correct the output of that system. After the TTS voice had been built, it also became possible to listen to the voice reading out the entered transcriptions.

Even before the first machine learning model had been trained for Bengali, we configured the transcription tool to provide some constraints on how words could be transcribed. Bengali, like most writing systems, has certain aspects that make it complex, while in other ways it is quite regular. As discussed above, the grapheme �a� (?) can have different pronunciations depending on context, but its pronunciation does not vary wildly: it is either silent or pronounced as a vowel, never as a consonant. By incorporating constraints on which graphemes can correspond to which phonemes, we can easily identify unlikely or erroneous transcriptions. This methodology has been in use at Google for several years.

The grapheme-phoneme correspondence varies along several dimensions, including regular words vs. abbreviations, and native words vs. loanwords. For example the word meaning "doctor" and pronounced /??k.?or/ can be written in several ways in Bengali: in Bengali script as ????? or as the abbreviation ??; and in Latin script as the English loanword doctor or as the abbreviation Dr. A TTS system should accept all ways of writing this word, hence all written variations are in our pronunciation lexicon.

A Bengali TTS voice should further be able to pronounce a variety of common brand names written in Latin script. The linguists from Project Unison therefore transcribed a few thousand such words phonemically into Bengali. For example, "WhatsApp" was transcribed /ho.a?s.�p/, and "Google" was straightforwardly transcribed as /gu.gol/ just as if it had been spelled ????.

Overall our linguists transcribed more than 65,000 Bengali words into phonemic notation. In an effort to contribute to the community working on speech synthesis, speech recognition, and related natural language efforts, we are releasing our Bengali pronunciation dictionary under a
Creative Commons License (CC BY 4.0). It is our hope that this will be a valuable resource for researchers and developers who are improving the state of spoken language systems.

Despite our efforts, this Bengali dictionary is incomplete and contains residual errors. As a work-in-progress it will continue to improve over time. We are hoping that other natural language and speech researchers will join us in making available more datasets under open licenses. As we refine our development process and extend it to more languages, we are planning on releasing additional datasets for other languages in the future.

NEXT UP: One Down, 299 to Go (Ep 4)

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