Abstract: Described herein are systems, methods, and apparatus for determining audio context between an audio source and an audio sink and selecting signal profiles based at least in part on that audio context. The signal profiles may include noise cancellation which is configured to facilitate operation within the audio context. Audio context may include user-to-user and user-to-device communications.

Abstract: Described herein are systems, methods, and apparatus for determining audio context between an audio source and an audio sink and selecting signal profiles based at least in part on that audio context. The signal profiles may include noise cancellation which is configured to facilitate operation within the audio context. Audio context may include user-to-user and user-to-device communications.

Abstract: A user can make a symbol with their hand, or other such gesture, at a distance from a computing device that can be captured by at least one imaging element of the device. The captured information can be analyzed to attempt to determine the location of distinguishing features of the symbol in the image information. The image information is then compared to hand gesture information stored in, for example, a library of hand gestures for the user. Upon identifying a match, an input to an application executing on the computing device is provided when the image information contains information matching at least one hand gesture with at least a minimum level of certainty. The hand gesture could include a single “static” gesture, such as a specific letter in sign language, for example, or include two or more “static” gestures. The gesture could also include motion, such as hand movement.

Abstract: Described herein are systems, methods, and apparatus for determining audio context between an audio source and an audio sink and selecting signal profiles based at least in part on that audio context. The signal profiles may include noise cancellation which is configured to facilitate operation within the audio context. Audio context may include user-to-user and user-to-device communications.

Abstract: Described herein are systems and methods for the generation and maintenance of language models. Language models are developed based at least in part on transaction data from one or more users. These transactions may include purchases and other interactions between one or more users and one or more online merchants. The language models may be associated with a particular user or group of users. Ongoing transactions may modify the language models. The language models may be used to recognize spoken input from the one or more users.

Abstract: Described herein are systems and methods for context-aware speech processing. A speech context is determined based on context data associated with a user uttering speech. The speech context and the speech uttered in that speech context may be used to build acoustic models for that speech context. An acoustic model for use in speech processing may be selected based on the determined speech context. A language model for use in speech processing may also be selected based on the determined speech context. Using the acoustic and language models, the speech may be processed to recognize the speech from the user.

Abstract: Described herein are systems, methods, and apparatus for determining audio context between an audio source and an audio sink and selecting signal profiles based at least in part on that audio context. The signal profiles may include noise cancellation which is configured to facilitate operation within the audio context. Audio context may include user-to-user and user-to-device communications.

Abstract: A user can make a symbol with their hand, or other such gesture, at a distance from a computing device that can be captured by at least one imaging element of the device. The captured information can be analyzed to attempt to determine the location of distinguishing features of the symbol in the image information. The image information is then compared to hand gesture information stored in, for example, a library of hand gestures for the user. Upon identifying a match, an input to an application executing on the computing device is provided when the image information contains information matching at least one hand gesture with at least a minimum level of certainty. The hand gesture could include a single “static” gesture, such as a specific letter in sign language, for example, or include two or more “static” gestures. The gesture could also include motion, such as hand movement.

Abstract: Described herein are systems, methods, and apparatus for determining audio context between an audio source and an audio sink and selecting signal profiles based at least in part on that audio context. The signal profiles may include noise cancellation which is configured to facilitate operation within the audio context. Audio context may include user-to-user and user-to-device communications.

Abstract: A user can make a symbol with their hand, or other such gesture, at a distance from a computing device that can be captured by at least one imaging element of the device. The captured information can be analyzed to attempt to determine the location of distinguishing features of the symbol in the image information. The image information is then compared to hand gesture information stored in, for example, a library of hand gestures for the user. Upon identifying a match, an input to an application executing on the computing device is provided when the image information contains information matching at least one hand gesture with at least a minimum level of certainty. The hand gesture could include a single “static” gesture, such as a specific letter in sign language, for example, or include two or more “static” gestures. The gesture could also include motion, such as hand movement.