Sri International

Patti J Price

Mitchel Weintraub

Horacio E Franco

Hy Murveit

Michael H Cohen

Leonardo Neumeyer

Jared C Bernstein

Vassilios Digalakis

Pronunciation quality is automatically evaluated for an utterance of speech based on one or more pronunciation scores. One type of pronunciation score is based on duration of acoustic units. Examples of acoustic units include phones and syllables. Another type of pronunciation score is based on a posterior probability that a piece of input speech corresponds to a certain model such as an HMM, given the piece of input speech. Speech may be segmented into phones and syllables for evaluation with respect to the models. The utterance of speech may be an arbitrary utterance made up of a sequence of words which had not been encountered before. Pronunciation scores are converted into grades as would be assigned by human graders. Pronunciation quality may be evaluated in a client-server language instruction environment.

Method and system for automatic text-independent grading of pronunciation for language instruction

Pronunciation quality is automatically evaluated for an utterance of speech based on one or more pronunciation scores. One type of pronunciation score is based on duration of acoustic units. Examples of acoustic units include phones and syllables. Another type of pronunciation score is based on a posterior probability that a piece of input speech corresponds to a certain model, such as a hidden Markov model, given the piece of input speech. Speech may be segmented into phones and syllable for evaluation with respect to the models. The utterance of speech may be an arbitrary utterance made up of a sequence of words which had not been encountered before. Pronunciation scores are converted into grades as would be assigned by human graders. Pronunciation quality may be evaluated in a client-server language instruction environment.

Method and apparatus for automatic text-independent grading of

An automatic speech recognition methodology, wherein words are modeled as probabilistic networks of allophones, collects nodes in the probabilistic network into equivalence classes when those nodes have the same allophonic choices governed by the same phonological rules. The allophonic choices allow for representation of dialectic pronunciation variations between different speakers. Training data is shared among nodes in an equivalence class so that accurate pronunciation probabilities may be determined even for words for which there is only a limited amount of training data. A method is used to determine probabilities for each of a multitude of pronunciation models for each word in the vocabulary, based on automatic extraction of linguistic knowledge from sets of phonological rules, in order to robustly and accurately model dialectal variation.

Method for recognizing speech using linguistically-motivated hidden

An automatic speech recognition methodology takes advantage of linguistic constraints wherein words are modeled as probabilistic networks of phonetic segments (herein phones), and each phone is represented as a context-independent hidden Markov phone model mixed with a number of context-dependent phone models. Recognition is based on use of methods to design phonological rule sets based on measures of coverage and overgeneration of pronunciations which achieves high coverage of pronunciations with compact representations. Further, a method estimates probabilities of the different possible pronunciations of words. A further method models cross-word coarticulatory effects. In a specific embodiment of the system, a specific method determines the single most-likely pronunciation of words. In further specific embodiments of the system, methods generate speaker-dependent pronunciation networks.

Growing community of inventors

Palo Alto, CA, United States of America

Patti J Price