Abstract: According to some aspects, a method of monitoring an acoustic environment of a mobile device, at least one computer readable medium encoded with instructions that, when executed, perform such a method and/or a mobile device configured to perform such a method is provided. The method comprises receiving acoustic input from the environment of the mobile device while the mobile device is operating in the low power mode, detecting whether the acoustic input includes a voice command based on performing a plurality of processing stages on the acoustic input, wherein at least one of the plurality of processing stages is performed while the mobile device is operating in the low power mode, and using at least one contextual cue to assist in detecting whether the acoustic input includes a voice command.

Abstract: According to some aspects, a method of monitoring an acoustic environment of a mobile device, at least one computer readable medium encoded with instructions that, when executed, perform such a method and/or a mobile device configured to perform such a method is provided. The method comprises receiving acoustic input from the environment of the mobile device while the mobile device is operating in the low power mode, detecting whether the acoustic input includes a voice command based on performing a plurality of processing stages on the acoustic input, wherein at least one of the plurality of processing stages is performed while the mobile device is operating in the low power mode, and using at least one contextual cue to assist in detecting whether the acoustic input includes a voice command.

Abstract: According to some aspects, a method of monitoring an acoustic environment of a mobile device, at least one computer readable medium encoded with instructions that, when executed, perform such a method and/or a mobile device configured to perform such a method is provided. The method comprises receiving acoustic input from the environment of the mobile device while the mobile device is operating in the low power mode, detecting whether the acoustic input includes a voice command based on performing a plurality of processing stages on the acoustic input, wherein at least one of the plurality of processing stages is performed while the mobile device is operating in the low power mode, and using at least one contextual cue to assist in detecting whether the acoustic input includes a voice command.

Abstract: Methods and apparatus for unsupervised wakeup of a device including receiving a first acoustic event at a first time and a second acoustic event at a second time, wherein the first and second acoustic events have scores above a first threshold identifying the first and second acoustic events as wakeup candidates for a wakeup phrase for an unsupervised wakeup of a device. It can be determined that the first acoustic event score is below a second threshold, which is higher than the first threshold and whether a difference between the first and second times is within a range to check for correlation in time between the first and second acoustic events. Occurrence of a wakeup event can be determined based upon the first and second times.

Abstract: Methods and apparatus for unsupervised wakeup of a device including receiving a first acoustic event at a first time and a second acoustic event at a second time, wherein the first and second acoustic events have scores above a first threshold identifying the first and second acoustic events as wakeup candidates for a wakeup phrase for an unsupervised wakeup of a device. It can be determined that the first acoustic event score is below a second threshold, which is higher than the first threshold and whether a difference between the first and second times is within a range to check for correlation in time between the first and second acoustic events. Occurrence of a wakeup event can be determined based upon the first and second times.

Abstract: Methods and apparatus for unsupervised wakeup of a device including receiving a first acoustic event at a first time and a second acoustic event at a second time, wherein scores of the first and second acoustic events are above a first threshold identifying the first and second acoustic events as wakeup candidates for a wakeup phrase for an unsupervised wakeup of a device. It is determined that the first acoustic event is above a second threshold, which is higher than the first threshold, and that the second acoustic event is above a third threshold, which is higher than the first threshold. Occurrence of a wakeup event can be determined based upon acoustic similarity of the events.

Abstract: Methods and apparatus for unsupervised wakeup of a device including receiving a first acoustic event at a first time and a second acoustic event at a second time, wherein scores of the first and second acoustic events are above a first threshold identifying the first and second acoustic events as wakeup candidates for a wakeup phrase for an unsupervised wakeup of a device. It is determined that the first acoustic event is above a second threshold, which is higher than the first threshold, and that the second acoustic event is above a third threshold, which is higher than the first threshold. Occurrence of a wakeup event can be determined based upon acoustic similarity of the events.

Abstract: Methods and apparatus for unsupervised wakeup of a device including receiving a first acoustic event at a first time and a second acoustic event at a second time, wherein the first and second acoustic events have scores above a first threshold identifying the first and second acoustic events as wakeup candidates for a wakeup phrase for an unsupervised wakeup of a device. It can be determined that the first acoustic event score is below a second threshold, which is higher than the first threshold and whether a difference between the first and second times is within a range to check for correlation in time between the first and second acoustic events. Occurrence of a wakeup event can be determined based upon the first and second times.

Abstract: Techniques disclosed herein include systems and methods that enable a voice trigger that wakes-up an electronic device or causes the device to make additional voice commands active, without manual initiation of voice command functionality. In addition, such a voice trigger is dynamically programmable or customizable. A speaker can program or designate a particular phrase as the voice trigger. In general, techniques herein execute a voice-activated wake-up system that operates on a digital signal processor (DSP) or other low-power, secondary processing unit of an electronic device instead of running on a central processing unit (CPU). A speech recognition manager runs two speech recognition systems on an electronic device. The CPU dynamically creates a compact speech system for the DSP. Such a compact system can be continuously run during a standby mode, without quickly exhausting a battery supply.

Abstract: Techniques disclosed herein include systems and methods that enable a voice trigger that wakes-up an electronic device or causes the device to make additional voice commands active, without manual initiation of voice command functionality. In addition, such a voice trigger is dynamically programmable or customizable. A speaker can program or designate a particular phrase as the voice trigger. In general, techniques herein execute a voice-activated wake-up system that operates on a digital signal processor (DSP) or other low-power, secondary processing unit of an electronic device instead of running on a central processing unit (CPU). A speech recognition manager runs two speech recognition systems on an electronic device. The CPU dynamically creates a compact speech system for the DSP. Such a compact system can be continuously run during a standby mode, without quickly exhausting a battery supply.

Abstract: Language model results are combined according to a combination expression to produce combined language model results for a set of candidates. A candidate is selected and the combination expression is adjusted using language model results associated with the selected candidate.

Abstract: System for segmenting text and identifying segment topics that match a user-specified topic. Topic tracking system creates a set of topic models from training text containing topic boundaries using a clustering algorithm. User supplies topic text. System creates a topic model of the topic text and adds the topic model to the set of topic models. User-supplied test text is segmented according to the set of topic models. Segments relating to the same topic as the topic text are selected.