Dragon Systems, Inc.

Voice Signal Technologies, Inc.

Other

Jonathan P Yamron

Laurence Steven Gillick

Steven A Wegmann

Daniel Lawrence Roth

Michael Jack Newman

Thomas E Lynch

James K Baker

Jonathan Hood Young

Toffee A Albina

Janet M Baker

Paul G Bamberg

Paul A Van Mulbregt

Marlboro B Moore, Iii

Paul E Rensing

Haakon L Chevalier

James Barnett

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 invention provides techniques for creating and using fragmented word models to increase the effective size of an active vocabulary of a speech recognition system. The active vocabulary represents all words and word fragments that the speech recognition system is able to recognize. Each word may be represented by a combination of acoustic models. As such, the active vocabulary represents the combinations of acoustic models that the speech recognition system may compare to a user's speech to identify acoustic models that best match the user's speech. The effective size of the active vocabulary may be increased by dividing words into constituent components or fragments (for example, prefixes, suffixes, separators, infixes, and roots) and including each component as a separate entry in the active vocabulary. Thus, for example, a list of words and their plural forms (for example, 'book, books, cook, cooks, hook, hooks, look and looks') may be represented in the active vocabulary using the words (for example, 'book, cook, hook and look') and an entry representing the suffix that makes the words plural (for example, &#x201c;+s&#x201d;, where the &#x201c;+&#x201d; preceding the &#x201c;s&#x201d; indicates that &#x201c;+s&#x201d; is a suffix). For a large list of words, and ignoring the entry associated with the suffix, this technique may reduce the number of vocabulary entries needed to represent the list of words considerably.

Expanding an effective vocabulary of a speech recognition system

System for segmenting text and identifying segment topics that match a user-specified topic. Topic tracking system creates a set of topic models from training text containing topic boundaries using a clustering algorithm. User supplies topic text. System creates a topic model of the topic text and adds the topic model to the set of topic models. User-supplied test text is segmented according to the set of topic models. Segments relating to the same topic as the topic text are selected.

Text segmentation and identification of topic using language models

In one aspect, the invention provides word recognition systems that operate to recognize an unrecognized or ambiguous word that occurs within a passage of words. The system can offer several words as choice words for inserting into the passage to replace the unrecognized word. The system can select the best choice word by using the choice word to extract from a reference source, sample passages of text that relate to the choice word. For example, the system can select the dictionary passage that defines the choice word. The system then compares the selected passage to the current passage, and generates a score that indicates the likelihood that the choice word would occur within that passage of text. The system can select the choice word with the best score to substitute into the passage. The passage of words being analyzed can be any word sequence including an utterance, a portion of handwritten text, a portion of typewritten text or other such sequence of words, numbers and characters. Alternative embodiments of the present invention are disclosed which function to retrieve documents from a library as a function of context.

Systems and methods for word recognition

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Sudbury, MA, United States of America

Jonathan P Yamron