Scansoft, Inc.

Lernout & Hauspie Speech Products N.v.

Ramesh Sarukkai

Vladimir Sejnoha

William F Ganong, Iii

Brian Wilson

Girija Yegnanarayanan

Manfred Grabherr

Tom Lynch

A speech recognition system recognizes an input utterance of spoken words. The system includes a set of word models for modeling vocabulary to be recognized, each word model being associated with a word in the vocabulary, each word in the vocabulary considered as a sequence of phones including a first phone and a last phone, wherein each word model begins in the middle of the first phone of its associated word and ends in the middle of the last phone of its associated word; a set of word connecting models for modeling acoustic transitions between the middle of a word's last phone and the middle of an immediately succeeding word's first phone; and a recognition engine for processing the input utterance in relation to the set of word models and the set of word connecting models to cause recognition of the input utterance.

Word boundary acoustic units

A method of a continuous speech recognition system is given for discriminatively training hidden Markov for a system recognition vocabulary. An input word phrase is converted into a sequence of representative frames. A correct state sequence alignment with the sequence of representative frames is determined, the correct state sequence alignment corresponding to models of words in the input word phrase. A plurality of incorrect recognition hypotheses is determined representing words in the recognition vocabulary that do not correspond to the input word phrase, each hypothesis being a state sequence based on the word models in the acoustic model database. A correct segment of the correct word model state sequence alignment is selected for discriminative training. A frame segment of frames in the sequence of representative frames is determined that corresponds to the correct segment. An incorrect segment of a state sequence in an incorrect recognition hypothesis is selected, the incorrect segment corresponding to the frame segment. A discriminative adjustment is performed on selected states in the correct segment and the corresponding states in the incorrect segment.

Discriminatively trained mixture models in continuous speech recognition

A modeless large vocabulary continuous speech recognition system is provided that represents an input utterance as a sequence of input vectors. The system includes a common library of acoustic model states for arrangement in sequences that form acoustic models. Each acoustic model is composed of a sequence of segment models and each segment model is composed of a sequence of model states. An input processor compares each vector in a sequence of input vectors to a set of model states in the common library to produce a match score for each model state in the set, reflecting the likelihood that a state is represented by a vector. The system also includes a plurality of recognition modules and associated recognition grammars. The recognition modules operate in parallel and use the match scores with the acoustic models to determine at least one recognition result in each of the recognition modules. The recognition modules includes a dictation module for producing at least one probable dictation recognition result, a select module for recognizing a portion of visually displayed text for processing with a command, and a command module for producing at least one probable command recognition result. An arbitrator uses an arbitration algorithm and a score ordered queue of recognition results, together with their associated recognition modules, to compare the recognition results of the recognition modules to select at least one system recognition result.

System and method for modeless large vocabulary speech recognition

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Billerica, MA, United States of America

Ramesh Sarukkai