Abstract: A computerized system performs word recognition. It does pattern matching to select which vocabulary words appears, according to the pattern matching, to most probably correspond to word signals it seeks to recognize. It produces an output corresponding to the vocabulary words selected at a movable cursor position in a body of text. The system improves its recognition by obtaining information about the linguistic context of the current cursor position in the body of text and using that information to vary the probability of which one or more words is selected by its pattern matching. The pattern matching can be speech recognition performed on signals representing the sound of spoken words.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: A speech recognition technique uses lexical tree pre-filtering to obtain lists of words for use in performing speech recognition. The lexical tree pre-filtering includes representing a vocabulary of words using a lexical tree and identifying a first subset of the vocabulary that may correspond to speech spoken beginning at a first time by propagating through the lexical tree information about the speech spoken beginning at the first time. A second subset of the vocabulary that may correspond to speech spoken beginning at a second time is identified by propagating through the lexical tree information about the speech spoken beginning at the second time. Words included in the speech are recognized by comparing speech spoken beginning at the first time with words from the first subset of the vocabulary and speech spoken beginning at the second time with words from the second subset of the vocabulary. The state of the lexical tree is not reset between identifying the first and second subsets.

Abstract: An element (e.g., a location pointer in a windowed graphical user interface) shown on a computer display is moved toward a target position. A user's input is recognized which identifies a subregion of the display in which the target position is located. The element is moved discontinuously to a location within the identified subregion. Then a next user's input is recognized which identifies a sub-subregion of the subregion of the display in which the target position is located. The element is moved discontinuously to a location within the identified sub-subregion. In another aspect, an element is dragged between two locations on a computer display. User commands are accepted which indicate the two locations. Then the element is dragged between them. The user commands identify a sequence of discontinuous jumps for moving the element to the indicated location.

Abstract: In a method for use in recognizing continuous speech, signals are accepted corresponding to interspersed speech elements including text elements corresponding to text to be recognized and command elements corresponding to commands to be executed. The elements are recognized. The recognized elements are acted on in a manner which depends on whether they represent text or commands.

Abstract: A method for use in recognizing speech in which signals are accepted corresponding to interspersed speech elements including text elements corresponding to text to be recognized and command elements to be executed. The elements are recognized. Modification procedures are executed in response to recognized predetermined ones of the command elements. The modification procedures include refraining from training speech models when the modification procedures do not correct a speech recognition error. In another aspect, the modification procedures include simultaneously modifying previously recognized ones of the text elements.

Abstract: Words are added to a speech recognition system vocabulary during user dictation by (a) extracting, from a multi-word user utterance, speech frames that correspond to each one of the one or more new words; and (b) building speech models for the one or more new words using the extracted speech frames.

Abstract: A system and associated methods for recognizing compound words from an utterance containing a succession of one or more words from a predetermined vocabulary. At least one of the words in the utterance is a compound word including at least two formatives in succession, wherein those formatives are words in the vocabulary.

Abstract: A computerized system time aligns frames of spoken training data against models of the speech sounds; automatically selects different sets of phonetic context classifications which divide the speech sound models into speech sound groups aligned against acoustically similar frames; creates model components from the frames aligned against speech sound groups with related classifications; and uses these model components to build a separate model for each related speech sound group. A decision tree classifies speech sounds into such groups, and related speech sound groups descend from common tree nodes. New speech samples time aligned against a given speech sound group's model update models of related speech sound groups, decreasing the training data required to adapt the system. The phonetic context classifications can be based on knowledge of which contextual features are associated with acoustic similarity.

Abstract: 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.

Abstract: The system of the invention relates to continuous speech pre-filtering systems for use in discrete and continuous speech recognition computer systems. The speech to be recognized is converted from utterances to frame data sets, which frame data sets are smoothed to generate a smooth frame model over a predetermined number of frames. A resident vocabulary is stored within the computer as clusters of word models which are acoustically similar over a succession of frame periods. A cluster score is generated by the system, which score includes the likelihood of the smooth frames evaluated using a probability model for the cluster against which the smooth frame model is being compared. Cluster sets having cluster scores below a predetermined acoustic threshold are removed from further consideration. The remaining cluster sets are unpacked for determination of a word score for each unpacked word.

Abstract: A tutorial instructs how to use a word recognition system, such as one for speech recognition. It specifies a set of allowed response words for each of a plurality of states. It sends messages on how to use the recognizer in certain states, and, in others, presents exercises in which the user is to enter signals representing expected words. It scores each such signal against word models to select which response word corresponds to it, and then advances to a state associated with that selected response. This scoring is performed against a large vocabulary even though only a small number of responses are allowed, and the signal is rejected if too many non-allowed words score better than any allowed word. The system comes with multiple sets of standard signal models; it scores each against a given user's signals, selects the set which scores best, and then performs adaptive and batch training upon that set.

Abstract: A continuous speech prefiltering system for use in continuous speech recognition computer systems. The speech to be recognized is converted from utterances to frame data sets, which frame data sets are smoothed to generate a smooth frame model over a predetermined number of frames. A resident vocabulary is stored within the computer as clusters of word models which are acoustically similar over a succession of frame periods. A cluster score is generated by the system, which score includes the likelihood of the smooth frames evaluated using a probability model for the cluster against which the smooth frame model is being compared. Cluster sets having cluster scores below a predetermined acoustic threshold are removed from further consideration. The remaining cluster sets are unpacked for determination of a word score for each unpacked word.

Abstract: A method for creating word models for a large vocabulary, natural language dictation system. A user with limited typing skills can create documents with little or no advance training of word models. As the user is dictating, the user speaks a word which may or may not already be in the active vocabulary. The system displays a list of the words in the active vocabulary which best match the spoken word. By keyboard or voice command, the user may choose the correct word from the list or may choose to edit a similar word if the correct word is not on the list. Alternately, the user may type or speak the initial letters of the word. Then the recognition algorithm is called again satisfying the initial letters, and the choices displayed again. A word list is then also displayed from a large backup vocabulary. The best words to display from the backup vocabulary are chosen using a statistical language model and optionally word models derived from a phonemic dictionary.

Abstract: The invention provides a method of deriving generally improved statistical acoustic model of a first class of speech sounds, given a limited amount of sampling data from that first class. This is done by combining a first statistic calculated from samples of that class of speech sounds with a corresponding second statistic calculated from samples of a second, broader, class of speech sounds. Preferably the second statistic is calculated from many more samples than the first statistic, so it has less sampling error that the first statistic, and preferably the second class is a super-set of the first class, so that the second statistic will provide information about the first class. In one embodiment, the invention combines statistics from the models of a plurality of first classes of speech sounds to reduce the sampling error of such statistics and thus improve the accuracy with which such models can be divided into groups of similar models.

Abstract: A method is provided for deriving acoustic word representations for use in speech recognition. Initial word models are created, each formed of a sequence of acoustic sub-models. The acoustic sub-models from a plurality of word models are clustered, so as to group acoustically similar sub-models from different words, using, for example, the Kullback-Leibler information as a metric of similarity. Then each word is represented by cluster spelling representing the clusters into which its acoustic sub-models were placed by the clustering. Speech recognition is performed by comparing sequences of frames from speech to be recognized against sequences of acoustic models associated with the clusters of the cluster spelling of individual word models.

Abstract: A speech recognition system which can perform multiple recognition passes on each word. If the recognizer is correct in its first pass, the operator may abort later passes by either pressing a key or speaking the next word. Otherwise, the operator may either wait for a second recognition pass to be performed against a larger vocabulary, or may specify one or more initial letters causing the second recognition pass to be performed against a vocabulary substantially restricted to words starting with those initial letters. Each time the user adds an additional letter to the initial string, any previous recognition is aborted and the re-recognition process is started anew with the new string. If the user types a control character after the initial string, then the string itself is used as the output of the recognizer. In one embodiment, a language model limits a relatively small vocabulary used in the first pass to the words most likely to occur given the language context of the dictated word.

Abstract: A first speech recognition method receives an acoustic description of an utterance to be recognized and scores a portion of that description against each of a plurality of cluster models representing similar sounds from different words. The resulting score for each cluster is used to calculate a word score for each word represented by that cluster. Preferably these word scores are used to prefilter vocabulary words, and the description of the utterance includes a succession of acoustic decriptions which are compared by linear time alignment against a succession of acoustic models. A second speech recognition method is also provided which matches an acoustic model with each of a succession of acoustic descriptions of an utterance to be recognized. Each of these models has a probability score for each vocabulary word. The probability scores for each word associated with the matching acoustic models are combined to form a total score for that word.