Abstract: Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Abstract: Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Abstract: A local feedback mechanism for customizing training models based on user data and directed user feedback is provided in speech recognition applications. The feedback data is filtered at different levels to address privacy concerns for local storage and for submittal to a system developer for enhancement of generic training models.

Abstract: Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Abstract: Techniques are described herein that are capable of implementing a context-aware digital personal assistant (DPA) that supports multiple accounts and/or facilitating interaction among digital personal assistants. For example, a user may be signed-in with accounts of a DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least the user's context. In another example, users who are signed-in with accounts of a DPA may share a user experience provided by the DPA. Content from content streams associated with the respective accounts may be selectively combined based on at least one or more of the users' context. In yet another example, a first DPA associated with a first user may be caused to perform an operation on behalf of a second DPA associated with a second user or to delegate the operation to the second DPA.

Abstract: A local feedback mechanism for customizing training models based on user data and directed user feedback is provided in speech recognition applications. The feedback data is filtered at different levels to address privacy concerns for local storage and for submittal to a system developer for enhancement of generic training models.

Abstract: A local feedback mechanism for customizing training models based on user data and directed user feedback is provided in speech recognition applications. The feedback data is filtered at different levels to address privacy concerns for local storage and for submittal to a system developer for enhancement of generic training models.

Abstract: Architecture for integrating application-dependent information into a constraints component at deployment time or when available. In terms of a general grammar, the constraints component can include or be a general grammar that comprises application-independent information and is structured in such a way that application-dependent information can be integrated into the general grammar without loss of fidelity. The general grammar includes a probability space and reserves a section of the probability space for the integration of application-dependent information. An integration component integrates the application-dependent information into the reserved section of the probability space for recognition processing. The application-dependent information is integrated into the reserved section of the probability space at deployment time or when available. The general grammar is structured to support the integration and improve the overall system.

Abstract: A local text to speech feedback loop is utilized to modify algorithms used in speech synthesis to provide a user with an improved experience. A remote text to speech feedback loop is utilized to aggregate local feedback loop data and incorporate best solutions into new improved text to speech engine for deployment.

Abstract: Intelligent speech recognition is used to provide users with the ability to utter more user friendly commands. Satisfaction is increased when a user can vocalize a subset of a formal command name and still have the intended command identified and processed. Moreover, greater accuracy in identifying a command application from a user's utterance can be achieved by ignoring command choices associated with unlikely user utterances. An intelligent speech recognition system can identify differing acceptable verbal command phrase forms, e.g., but not limited to, complete commands, command subsequences and command subsets, for different commands supported by the system. Subset blocking words are identified for assistance in reducing the ambiguity in matching user verbal command phrases with valid commands supported by the intelligent speech recognition system.

Abstract: Architecture for integrating application-dependent information into a constraints component at deployment time or when available. In terms of a general grammar, the constraints component can include or be a general grammar that comprises application-independent information and is structured in such a way that application-dependent information can be integrated into the general grammar without loss of fidelity. The general grammar includes a probability space and reserves a section of the probability space for the integration of application-dependent information. An integration component integrates the application-dependent information into the reserved section of the probability space for recognition processing. The application-dependent information is integrated into the reserved section of the probability space at deployment time or when available. The general grammar is structured to support the integration and improve the overall system.

Abstract: A local feedback mechanism for customizing training models based on user data and directed user feedback is provided in speech recognition applications. The feedback data is filtered at different levels to address privacy concerns for local storage and for submittal to a system developer for enhancement of generic training models.

Abstract: A local text to speech feedback loop is utilized to modify algorithms used in speech synthesis to provide a user with an improved experience. A remote text to speech feedback loop is utilized to aggregate local feedback loop data and incorporate best solutions into new improved text to speech engine for deployment.

Abstract: A method for compressing multiple dimensional gaussian distributions with diagonal covariance matrixes includes clustering a plurality of gaussian distributions in a multiplicity of clusters for each dimension. Each cluster can be represented by a centroid having a mean and a variance. A total decrease in likelihood of a training dataset is minimized for the representation of the plurality of gaussian distributions.

Abstract: Intelligent speech recognition is used to provide users with the ability to utter more user friendly commands. Satisfaction is increased when a user can vocalize a subset of a formal command name and still have the intended command identified and processed. Moreover, greater accuracy in identifying a command application from a user's utterance can be achieved by ignoring command choices associated with unlikely user utterances. An intelligent speech recognition system can identify differing acceptable verbal command phrase forms, e.g., but not limited to, complete commands, command subsequences and command subsets, for different commands supported by the system. Subset blocking words are identified for assistance in reducing the ambiguity in matching user verbal command phrases with valid commands supported by the intelligent speech recognition system.

Abstract: A method and apparatus for speaker recognition is provided that matches the noise in training data to noise in testing data using spectral addition. Under spectral addition, the mean and variance for a plurality of frequency components are adjusted in the training data and the test data so that each mean and variance is matched in a resulting matched training signal and matched test signal. The adjustments made to the training data and test data add to the mean and variance of the training data and test data instead of subtracting from the mean and variance.

Abstract: A method and apparatus for speaker recognition is provided that matches the noise in training data to noise in testing data using spectral addition. Under spectral addition, the mean and variance for a plurality of frequency components are adjusted in the training data and the test data so that each mean and variance is matched in a resulting matched training signal and matched test signal. The adjustments made to the training data and test data add to the mean and variance of the training data and test data instead of subtracting from the mean and variance.

Abstract: A method and apparatus for training and using a pattern recognition model are provided. Under the invention, additive noise that matches noise expected in a test signal is included in a training signal. The noisy training signal is passed through one or more noise reduction techniques to produce pseudo-clean training data. The pseudo-clean training data is used to train the pattern recognition model. When the test signal is received, it is passed through the same noise reduction techniques used on the noisy training signal. This produces pseudo-clean test data, which is applied to the pattern recognition model. Under one embodiment, sets of training data are produced with each set containing a different type of noise.

Abstract: A method for compressing multiple dimensional gaussian distributions with diagonal covariance matrixes includes clustering a plurality of gaussian distributions in a multiplicity of clusters for each dimension. Each cluster can be represented by a centroid having a mean and a variance. A total decrease in likelihood of a training dataset is minimized for the representation of the plurality of gaussian distributions.

Abstract: Formants, corresponding to input speech units based either on a known text or the results of a speech recognition procedure, are generated from a formant synthesizer. A frequency response is generated based on the synthesized formants. A second frequency response is generated based on a speech signal which is received and which corresponds to utterances of speech units. The synthesized formants are modified based on a comparison of the frequency response corresponding to the synthesized formants and specific proportional characteristics of a frequency response of the input speech signal. In one illustrative embodiment, the comparison is then recalculated and further modifications are made accordingly to improve accuracy. In one illustrative embodiment, time aligning and frequency warping are utilized as modification functions.