Abstract: A natural language business system and method is developed to understand the underlying meaning of a person's speech, such as during a transaction with the business system. The system includes a speech recognition engine, and action classification engine, and a control module. The control module causes the system to execute an inventive method wherein the speech recognition and action classification models may be recursively optimized on an unisolated performance metric that is pertinent to the overall performance of the natural language business system, as opposed to the isolated model-specific criteria previously employed.

Abstract: A natural language business system and method is developed to understand the underlying meaning of a person's speech, such as during a transaction with the business system. The system includes a speech recognition engine, and action classification engine, and a control module.

Abstract: Techniques for improving an automatic baseform generation system. More particularly, the invention provides techniques for reducing insertion of spurious speech events in a word or phone sequence generated by an automatic baseform generation system. Such automatic baseform generation techniques may be accomplished by enhancing the scores of long-lasting speech events with respect to the scores of short-lasting events. For example, this may be achieved by merging competing candidates that relate to the same speech event (e.g., phone or word) and that overlap in time into a single candidate, the score of which may be equal to the sum of the scores of the merged candidates.

Abstract: Methods and arrangements for enhancing speech recognition in noisy environments, via providing at least one initial Compound Gaussian Mixture model, applying an adaptation algorithm to at least one item associated with speech enrollment data and to the at least one initial Compound Gaussian Mixture model to yield an intermediate output, and mathematically combining the at least one initial Compound Gaussian Mixture model with the intermediate output to yield an adapted Compound Gaussian Mixture model.

Abstract: Techniques for improving an automatic baseform generation system. More particularly, the invention provides techniques for reducing insertion of spurious speech events in a word or phone sequence generated by an automatic baseform generation system. Such automatic baseform generation techniques may be accomplished by enhancing the scores of long-lasting speech events with respect to the scores of short-lasting events. For example, this may be achieved by merging competing candidates that relate to the same speech event (e.g., phone or word) and that overlap in time into a single candidate, the score of which may be equal to the sum of the scores of the merged candidates.

Abstract: Techniques for improving an automatic baseform generation system. More particularly, the invention provides techniques for reducing insertion of spurious speech events in a word or phone sequence generated by an automatic baseform generation system. Such automatic baseform generation techniques may be accomplished by enhancing the scores of long-lasting speech events with respect to the scores of short-lasting events. For example, this may be achieved by merging competing candidates that relate to the same speech event (e.g., phone or word) and that overlap in time into a single candidate, the score of which may be equal to the sum of the scores of the merged candidates.

Abstract: A technique for separating a signal associated with a first source from a mixture of the first source signal and a signal associated with a second source comprises the following steps/operations. First, two signals respectively representative of two mixtures of the first source signal and the second source signal are obtained. Then, the first source signal is separated from the mixture in a non-linear signal domain using the two mixture signals and at least one known statistical property associated with the first source and the second source, and without a need to use a reference signal.

Abstract: Methods and apparatus for automatically deriving multiple phonetic baseforms of a word from a speech utterance of this word are provided in accordance with the present invention. In one embodiment, a method of automatically generating two or more phonetic baseforms from a spoken utterance representing a word includes the steps of: transforming the spoken utterance into a stream of acoustic observations; generating two or more strings of subphone units, wherein each string of subphone units represents a string of subphone units substantially maximizing a log-likelihood of the stream of acoustic observations, and wherein the log-likelihood is computed as a weighted sum of a transition score associated with a transition model and of an acoustic score associated with an acoustic model; and converting the two or more strings of subphone units into two or more phonetic baseforms.

Abstract: Techniques for improving an automatic baseform generation system. More particularly, the invention provides techniques for reducing insertion of spurious speech events in a word or phone sequence generated by an automatic baseform generation system. Such automatic baseform generation techniques may be accomplished by enhancing the scores of long-lasting speech events with respect to the scores of short-lasting events. For example, this may be achieved by merging competing candidates that relate to the same speech event (e.g., phone or word) and that overlap in time into a single candidate, the score of which may be equal to the sum of the scores of the merged candidates.

Abstract: Methods and arrangementgs for enhancing speech recognition in noisy environments, via providing providing at least one initial Compound Gaussian Mixture model, applying an adaptation algorithm to at least one item associated with speech enrollment data and to the at least one initial Compound Gaussian Mixture model to yield an intermediate output, and mathematically combining the at least one initial Compound Gaussian Mixture model with the intermediate output to yield an adapted Compound Gaussian Mixture model.

Abstract: A technique for separating a signal associated with a first source from a mixture of the first source signal and a signal associated with a second source comprises the following steps/operations. First, two signals respectively representative of two mixtures of the first source signal and the second source signal are obtained. Then, the first source signal is separated from the mixture in a non-linear signal domain using the two mixture signals and at least one known statistical property associated with the first source and the second source, and without a need to use a reference signal.