Dragon Systems, Inc.

Joel W Parke

Frank J McGrath

Joel M Gould

Steven D Squires

Jed M Roberts

James K Baker

Peter S Heitman

Elizabeth E Steele

Dean G Sturtevant

Charles E Ingold

Stijn Van Even

Gregory J Gadbois

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string; score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off, store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

Apparatuses and methods for training and operating speech recognition

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string, score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off; store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

Apparatuses and method for training and operating speech recognition

A method of adding a word to a speech recognition vocabulary includes creating a collection of possible phonetic pronunciations from a spelling of the word and using speech recognition to find a pronunciation from the collection that best matches an utterance of the word. The collection is created by comparing the spelling to a rules list of letter strings with associated phonemes. The list is searched for a letter string from the spelling of length greater than one letter. The collection is limited to phonetic pronunciations containing phonemes associated with the letter string of length greater than one. In another method, a net of possible phonetic pronunciations of the word is created from the spelling and speech recognition is used to find the pronunciation from the net that best matches the utterance of the word. The invention also features methods of assigning a pre-filtering class to a word.

Pronunciation generation in speech recognition

A word recognition system is disclosed for converting spoken utterances into either text or commands. The system runs on a platform capable of running a plurality applications. Text and commands are sent from a word recognition application to one or more user applications. In addition, information pertaining to the state of the user applications is sent back to the word recognition application. Word recognition probabilities are modified based the information received from the user applications. As a result, the probabilities of recognizing a spoken utterance as a particular command will be greater when that command is active in the user application then when it is not active. Also, text strings will be assigned higher probabilities when they are appropriate for the present state of the user application.

Method of speech command recognition with dynamic assignment of

A word recognition system detects the computational resources available to it, such as the speed or number of processors, or whether there is DSP hardware, and alters the instructions it executes in response. The system can be a word recognition program designed to run on different computers having different computational resources. The program receives user generated word signals representing words to be recognized; performs pattern matching on them to select which vocabulary words most probably correspond to such word signals; detects if certain computational resources are available; and varies the instructions it executes in response. In many embodiments the system is a speech recognition program. The word recognition program can vary the computational intensity of its signal processing as a function of available computational resources. Preferably it can match the same word models against representations of word signals produced both by its more and less intensive signal processing. For example, the more intense signal processing might take more frequent FFTs, but average them over the same time period at which the less intense signal processing takes FFTs, so both can represent word signals with the same data structure. If the program detects both a CPU and a DSP processor, it can cause the DSP to determine when the program should interrupt the CPU. The program can also vary the rate at which it filters relatively low scoring words out of consideration during the recognition process as a function of the level of available computational resources.

Word recognition system which alters code executed as a function of

A computerized speech recognition system creates acoustic models of phrases by concatenating acoustic models for individual words. The system stores an acoustic word model and spelling for each of its vocabulary words. When it receives the spelling of a multi-word phrase to be treated as a new vocabulary word, it stores that multi-word spelling as the spelling of the new vocabulary word, and a new acoustic model created by concatenating the acoustic word models of previous vocabulary words whose spellings correspond to words in the multi-word spelling as the acoustic model for the new word. The system can then perform speech recognition by comparing acoustic signals against the word models of stored vocabulary words, including those representing such multi-word phrases. Preferably when a multi-word model is formed, the individual acoustic models concatenated are modified to represent the coarticulation which takes place between words spoken continuously. This can be done by representing word models as sequences of individual phonemes, individual phonemes by phoneme-in-context models, and coarticulation by modifying the phoneme-in-context models of phonemes adjacent the boundary between concatenated words models to reflect the context of the phonemes on the other side of such word boundaries. The system can be a discrete word recognizer. The multi-word phrases can be selected by a user or be obtained from other programs running on the same computer as the speech recognizer, such as from one or more commands available in another program.

Speech recognition system which creates acoustic models by concatenating

A word recognition system can: respond to the input of a character string from a user by limiting the words it will recognize to words having a related, but not necessarily the same, string; score signals generated after a user has been prompted to generate a given word against words other than the prompted word to determine if the signal should be used to train the prompted word; vary the number of signals a user is prompted to generate to train a given word as a function of how well the training signals score against each other or prior models for the prompted word; create a new acoustic model of a phrase by concatenating prior acoustic models of the words in the phrase; obtain information from another program running on the same computer, such as its commands or the context of text being entered into it, and use that information to vary which words it can recognize; determine which program unit, such as an application program or dialog box, currently has input focus on its computer and create a vocabulary state associated with that program unit into which vocabulary words which will be made active when that program group has the focus can be put; detect the available computational resources and alter the instructions it executes in response; test if its ability to respond to voice input has been shut off without user confirmation, and, if so, turn that ability back on and prompt the user to confirm if that ability is to be turned off; store both a first and a second set of models for individual vocabulary words and enable a user to selectively cause the recognizer to disregard the second set of models for a selected word; and/or score a signal representing a given word against models for that word from different word model sets to select which model should be used for future recognition.

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Marlboro, MA, United States of America

Joel W Parke