Voice Signal Technologies, Inc.

Other

Dragon Systems, Inc.

Steven A Wegmann

Laurence Steven Gillick

Jonathan P Yamron

Daniel Lawrence Roth

Michael Jack Newman

Thomas E Lynch

A method for speech recognition. The method uses a single pronunciation estimator to train acoustic phoneme models and recognize utterances from multiple languages. The method includes accepting text spellings of training words in a plurality of sets of training words, each set corresponding to a different one of a plurality of languages. The method also includes, for each of the sets of training words in the plurality, receiving pronunciations for the training words in the set, the pronunciations being characteristic of native speakers of the language of the set, the pronunciations also being in terms of subword units at least some of which are common to two or more of the languages. The method also includes training a single pronunciation estimator using data comprising the text spellings and the pronunciations of the training words.

Multilingual speech recognition

The error rate of a pronunciation guesser that guesses the phonetic spelling of words used in speech recognition is improved by causing its training to weigh letter-to-phoneme mappings used as data in such training as a function of the frequency of the words in which such mappings occur. Preferably the ratio of the weight to word frequency increases as word frequencies decreases. Acoustic phoneme models for use in speech recognition with phonetic spellings generated by a pronunciation guesser that makes errors are trained against word models whose phonetic spellings have been generated by a pronunciation guesser that makes similar errors. As a result, the acoustic models represent blends of phoneme sounds that reflect the spelling errors made by the pronunciation guessers. Speech recognition enabled systems are made by storing in them both a pronunciation guesser and a corresponding set of such blended acoustic models.

Training and using pronunciation guessers in speech recognition

The content of a speech sample is recognized using a computer system by evaluating the speech sample against a nonparametric set of training observations, for example, utterances from one or more human speakers. The content of the speech sample is recognized based on the evaluation results. The speech recognition process also may rely on a comparison between the speech sample and a parametric model of the training observations.

Speech recognition using nonparametric speech models

Growing community of inventors

Cambridge, MA, United States of America

Steven A Wegmann