Abstract: A wearable computer is configured to use beamforming techniques to isolate a user's speech from extraneous audio signals occurring within a physical environment. A microphone array of the wearable computer may generate audio signal data from an utterance from a user's mouth. A motion sensor(s) of the wearable computer may generate motion data from movement of the wearable computer. This motion data may be used to determine a direction vector pointing from the wearable computer to the user's mouth, and a beampattern may be defined that has a beampattern direction in substantial alignment with the determined direction vector to focus the microphone array on the user's mouth for speech isolation.

Abstract: A wearable computer is configured to use beamforming techniques to isolate a user's speech from extraneous audio signals occurring within a physical environment. A microphone array of the wearable computer may generate audio signal data from an utterance from a user's mouth. A motion sensor(s) of the wearable computer may generate motion data from movement of the wearable computer. This motion data may be used to determine a direction vector pointing from the wearable computer to the user's mouth, and a beampattern may be defined that has a beampattern direction in substantial alignment with the determined direction vector to focus the microphone array on the user's mouth for speech isolation.

Abstract: Features are disclosed for managing the use of speech recognition models and data in automated speech recognition systems. Models and data may be retrieved asynchronously and used as they are received or after an utterance is initially processed with more general or different models. Once received, the models and statistics can be cached. Statistics needed to update models and data may also be retrieved asynchronously so that it may be used to update the models and data as it becomes available. The updated models and data may be immediately used to re-process an utterance, or saved for use in processing subsequently received utterances. User interactions with the automated speech recognition system may be tracked in order to predict when a user is likely to utilize the system. Models and data may be pre-cached based on such predictions.

Abstract: A wearable computer is configured to use beamforming techniques to isolate a user's speech from extraneous audio signals occurring within a physical environment. A microphone array of the wearable computer may generate audio signal data from an utterance from a user's mouth. A motion sensor(s) of the wearable computer may generate motion data from movement of the wearable computer. This motion data may be used to determine a direction vector pointing from the wearable computer to the user's mouth, and a beampattern may be defined that has a beampattern direction in substantial alignment with the determined direction vector to focus the microphone array on the user's mouth for speech isolation.

Abstract: Features are provided for selectively scoring portions of user utterances based at least on articulatory features of the portions. One or more articulatory features of a portion of a user utterance can be determined. Acoustic models or subsets of individual acoustic model components (e.g., Gaussians or Gaussian mixture models) can be selected based on the articulatory features of the portion. The portion can then be scored using a selected acoustic model or subset of acoustic model components. The process may be repeated for the multiple portions of the utterance, and speech recognition results can be generated from the scored portions.

Abstract: Features are disclosed for managing the use of speech recognition models and data in automated speech recognition systems. Models and data may be retrieved asynchronously and used as they are received or after an utterance is initially processed with more general or different models. Once received, the models and statistics can be cached. Statistics needed to update models and data may also be retrieved asynchronously so that it may be used to update the models and data as it becomes available. The updated models and data may be immediately used to re-process an utterance, or saved for use in processing subsequently received utterances. User interactions with the automated speech recognition system may be tracked in order to predict when a user is likely to utilize the system. Models and data may be pre-cached based on such predictions.

Abstract: Features are disclosed for managing the use of speech recognition models and data in automated speech recognition systems. Models and data may be retrieved asynchronously and used as they are received or after an utterance is initially processed with more general or different models. Once received, the models and statistics can be cached. Statistics needed to update models and data may also be retrieved asynchronously so that it may be used to update the models and data as it becomes available. The updated models and data may be immediately used to re-process an utterance, or saved for use in processing subsequently received utterances. User interactions with the automated speech recognition system may be tracked in order to predict when a user is likely to utilize the system. Models and data may be pre-cached based on such predictions.