Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

Abstract: Systems and methods for voice recognition determine energy levels for speech and noise and generate adaptive thresholds based on the determined energy levels. The adaptive thresholds are applied to determine the presence of speech and to generate noise-dependent triggers for indicating the presence of speech during high-noise conditions. In an embodiment, the signal energy is averaged in the presence of speech and in the presence of background noise. Audio energy calculations may be made by averaging via a sliding window or via a memory filter.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

Abstract: A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

Abstract: A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.

Abstract: A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

Abstract: A method and apparatus for adjusting a trigger parameter related to voice recognition processing includes receiving into the device an acoustic signal comprising a speech signal, which is provided to a voice recognition module, and comprising noise. The method further includes determining a noise profile for the acoustic signal, wherein the noise profile identifies a noise level for the noise and identifies a noise type for the noise based on a frequency spectrum for the noise, and adjusting the voice recognition module based on the noise profile by adjusting a trigger parameter related to voice recognition processing.

Abstract: A method include placing a first processor in a sleep operating mode and running a second processor that is operative to wake the first processor from the sleep operating mode in response to a speech command phrase. The method includes identifying, by the second processor, a speech command phrase segment and performing a control operation in response to detecting the segment in detected speech. The control operation is performed while the first processor is maintained in the sleep operating mode.