Abstract: A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.

Abstract: A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.

Abstract: A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.

Abstract: A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.

Abstract: A distributed pattern recognition training method includes providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites. The method also includes communicating from the at least one central pattern analysis node to the plurality of peripheral data analysis a plurality of kernel-based pattern elements. The method further includes performing a plurality of iterations of pattern template training at each of the plurality of peripheral data analysis sites.

Abstract: A speech recognition method, system and program product, the method in one embodiment comprising: obtaining input speech data; initiating a first speech recognition search process with at least one hypothesis; initiating a second speech recognition search process with a plurality of hypotheses; obtaining partial results from the second speech recognition search process, where the partial results include an evaluation of at least one hypothesis that the first speech recognition search process has not evaluated at this point in time; and utilizing the partial results to alter the first speech recognition search process.

Abstract: A speech recognition method obtains a list of target speech element sequences each containing at least one speech element. For each target speech element sequence, a forward sequence extension model and a backward sequence extension model is obtained. At least one spotted target speech element sequence is found in a set of acoustic observations by matching it against the sequence of speech element models. From the set of acoustic observations, the set of acoustic observations preceding and following the at least one spotted target speech element sequence is obtained. At least one hypothesis of a longer speech element sequence containing the at least one spotted speech element sequence is obtained as a proper subsequence in which the at least one longer speech element sequence is consistent with at least one of the forward sequence extension model and the backward sequence extension model.

Abstract: A method of speech recognition obtains acoustic data from a plurality of conversations. A plurality of pairs of utterances are selected from the plurality of conversations. At least one portion of the first utterance of the pair of utterances is dynamically aligned with at least one portion of the second utterance of the pair of utterance, and an acoustic similarity is computed. At least one pair that includes a first portion from a first utterance and a second portion from a second utterance is chosen, based on a criterion of acoustic similarity. A common pattern template is created from the first portion and the second portion.

Abstract: A word recognition method and system includes obtaining a first portion of a sentence and a second portion of the sentence. The first portion of the sentence is used to obtain a pointer to respective list of second portions of sentences that are complementary to the first portion of the sentence. A match is determined to one of the second portions of sentences from the list based on information obtained from the second portion of the sentence. An error correction capability for dealing with one word errors in the sentence for checking against the database is also attainable.

Abstract: A method for performing speech recognition of a user's speech includes performing a first speech recognition process on each utterance of the user's speech, using acoustic models that are based on training data of non-discrete utterances. The method also includes performing a second speech recognition process on each utterance of the user's speech, using acoustic models that are based on training data of discrete utterances. The method further includes obtaining a first match score for each utterance of the user's speech from the first speech recognition process and obtaining a second match score for each utterance of the user's speech from the second speech recognition process. The method also includes determining a highest match score from the first and second match scores. The method further includes providing a speech recognition output for the user's speech, based on highest match scores of each utterance as obtained from the first and second speech recognition processes.

Abstract: A speech recognition method, system, and program product, the method comprising in one embodiment: obtaining a frame match score for each of a plurality of different speech elements for a frame; obtaining a scrunched score for each of a plurality of the frame match scores for the frame, wherein a scrunched score means applying a non-linear transformation to each of the frame match scores so that frame match score differences among relatively good competing frame matches are reduced while the score differences between good frame matches and the poor frame matches is substantially maintained or increased, wherein a relatively good frame match score is determined based on a criterion; for each of a plurality of hypotheses, accumulating the scrunched scores for frames of the hypothesis to obtain a hypothesis scrunched score for the hypothesis; selecting a plurality of hypotheses with better hypothesis scrunched scores as compared to the accumulated scrunched scores for other hypotheses; for each of the selected h

Abstract: A speech recognition method includes a step of receiving a phonetic sequence output by a phonetic recognizer. The method also includes a step of matching the phonetic sequence with one of a plurality of reference phoneme sequences stored in a reference list that matches closest thereto. At least one of the plurality of reference phoneme sequences stored in the reference list includes additional information with respect to a phonetic sequence that is capable of being output by the phonetic recognizer.

Abstract: A method for interpreting information provided over a telephone line from a customer includes providing at least a portion of an utterance made by the customer to a speech recognizer, at a same time the utterance is being heard on the telephone line by a call center operator. The method also includes processing, by the speech recognizer, the portion of the utterance made by the customer, in order to obtain a speech recognition result. The method further includes providing the speech recognition result to the call center operator, to assist the call center operator in discerning the utterance made by the customer.

Abstract: A method, program product and system for speech recognition for use with a base speech recognition process, but which does not affect scoring models in the base speech recognition process, the method comprising in one embodiment: obtaining an output hypothesis from a base speech recognition process that uses a first set of scoring models; obtaining a set of alternative hypotheses; scoring the set of alternative hypotheses based on a second set of different scoring models that is separate from and external to the base speech recognition process and does not affect the scoring models thereof; and selecting a hypothesis with a best score.

Abstract: A database retrieval system obtains telephone directory information, and includes a speech receiving unit that outputs an acoustic observation sequence corresponding to a speaker's utterance of a first name and last name of someone for whom a telephone number is desired. The system also includes a speech recognition processing unit that performs speech recognition processing on acoustic observations, to obtain a list of candidate hypotheses, and to obtain a match score for each candidate hypothesis. The system further includes a hypothesis evaluating unit that determines whether any candidate hypothesis has an initial for a first name part of the corresponding database entry, to generate all consistent first names, and to obtain a plurality of generated hypotheses corresponding to each of the generated first names. The speech recognition processing unit performs another speech recognition processing on the acoustic observation sequence, to obtain a match score for each generated hypothesis.

Abstract: A method, program product, and system for speech recognition, the method comprising in one embodiment pruning a hypothesis based on a first criteria; storing information about the pruned hypothesis; and reactivating the pruned hypothesis if a second criterion is met. In an embodiment, the first criteria may be that another hypothesis has a better score at that time by some predetermined amount. In an embodiment, the stored information may comprise at least one of a score for the pruned hypothesis, an identification of the hypothesis that caused the pruning and the frame in which the pruning took place. In a further embodiment, the reactivating step may use at least some of the stored information about the pruned hypothesis in performing the reactivation and the second criteria may be that a revised score for the hypothesis that caused the pruning is worse by some predetermined amount from an original expected score calculated for that hypothesis.

Abstract: A speech recognition system includes an input unit configured to receive a sequence of acoustic observations. The system also includes a target pattern detecting unit configured to detect whether at least one of a set of prescribed patterns occurs in the sequence of acoustic observations, and for outputting a target detection signal as a result thereof. The system further includes a priority queue search unit configured to receive the target detection signal as output by the target pattern detecting unit, to separate the sequence of acoustic observations into subsequences of acoustic observations separated by the at least one prescribed pattern, and to include an entry in a priority queue for each of the subsequences. The priority queue search unit is configured to determine whether or not one beam of nodes corresponding to one entry in the priority queue can be joined with another beam of nodes corresponding to another entry in the priority queue.

Abstract: A speech recognition method includes receiving a sequence of acoustic observations. The method also includes detecting whether or not at least one of a set of prescribed patterns occurs in the sequence of acoustic observations. The method further includes, based on the detecting result, setting an anchor for each of the set of prescribed patterns detected, and splitting up the sequence of acoustic observations into separate subsequences separated by the anchor. The method also includes performing a speech recognition processing on each of the separate subsequences, in sequence, and joining that information along with information of the anchor, to obtain speech recognition processing of an entirely of the sequence of acoustic observations.

Abstract: A speech recognition method, system and program product for the context of an existing model for a speech element, the method comprising in one embodiment: detecting an unusual instance of the speech; creating a new model to recognize the unusual instance of the speech element; computing a score for both the existing model by itself and the new model on new speech data; determining a comparative accuracy parameter for each of the models; and selecting to keep the existing model, or to keep the new model, or to keep both the existing model and the new model based on the comparative accuracy parameters of the respective models.

Abstract: A word recognition method and system includes obtaining a first portion of a sentence and a second portion of the sentence. The first portion of the sentence is used to obtain a pointer to respective list of second portions of sentences that are complementary to the first portion of the sentence. A match is determined to one of the second portions of sentences from the list based on information obtained from the second portion of the sentence. An error correction capability for dealing with one word errors in the sentence for checking against the database is also attainable.