Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. A user utterance may be processed by a speech recognition computing device. One or more acoustic features associated with the user utterance may be used to determine whether one or more actions are to be performed based on a transcription of the user utterance.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. A user utterance may be processed by a speech recognition computing device. One or more acoustic features associated with the user utterance may be used to determine whether one or more actions are to be performed based on a transcription of the user utterance.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: Various implementations described herein are directed to determining variations or changes to a predetermined programming schedule. In accordance with one method, a predicted end time of video content may be determined to be later than a scheduled end time of the video content. Also, it may be determined that video content scheduled to be displayed on a first stream or channel has been moved to a second stream or channel. A scheduled recording or transmission time of video content may be altered based on detected changes to the predetermined programming schedule. A program listing such as an electronic program guide may be revised based on detected changes to the predetermined programming schedule.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: Provided herein are systems and methods for improved speech and command detection. For example, a user utterance may be received by a voice-enabled device. The voice-enabled device and associated system may determine that a first portion of the utterance comprises a complete command, and begin processing the command for execution. Thereafter, the device may receive an additional utterance and determine the additional utterance to be a second portion, related to the first portion, and together with the first portion comprise a different command. The device and associated system can then adjust and process the intended command.

Abstract: Methods and systems are disclosed for determining a probability that a prospective wakeup or activation word is not an actual wakeup or activation word for a user device but instead is a word that has characteristics of, or otherwise sounds similar to, a wakeup or activation word and is received as a result of output of a content asset as opposed to being spoken by a user of the user device. Audio data associated with output of a content asset may be received and evaluated to determine if a prospective wakeup word in the audio data is an actual wakeup word or is, instead, not a wakeup word.

Abstract: Methods, systems, and apparatuses for improved speech recognition and transcription of user utterances are described herein. User utterances may be processed by a speech recognition computing device as well as an acoustic model. The acoustic model may be trained using historical user utterance data and machine learning techniques. The acoustic model may be used to determine whether a transcription determined by the speech recognition computing device should be overridden with an updated transcription.

Abstract: Various implementations described herein are directed to determining variations or changes to a predetermined programming schedule. In accordance with one method, a predicted end time of video content may be determined to be later than a scheduled end time of the video content. Also, it may be determined that video content scheduled to be displayed on a first stream or channel has been moved to a second stream or channel. A scheduled recording or transmission time of video content may be altered based on detected changes to the predetermined programming schedule. A program listing such as an electronic program guide may be revised based on detected changes to the predetermined programming schedule.

Abstract: An audio file associated with a user voice query may be received at a user device. The audio file may be compared to a plurality of references, such as cache entries, corresponding to a plurality of other voice queries. Based on a determination that the voice query corresponds to one of the references, an operation associated with the voice query may be executed. An indication may be received that the operation was not an intended operation associated with the voice query. Based on receiving this indication, the incorrectly identified operation, associated reference, e.g., voice query, may be disabled for the user or the device. However, the cache entry may remain enabled for one or more of a plurality of other devices.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: Various implementations described herein are directed to determining variations or changes to a predetermined programming schedule. In accordance with one method, a predicted end time of video content may be determined to be later than a scheduled end time of the video content. Also, it may be determined that video content scheduled to be displayed on a first stream or channel has been moved to a second stream or channel. A scheduled recording or transmission time of video content may be altered based on detected changes to the predetermined programming schedule. A program listing such as an electronic program guide may be revised based on detected changes to the predetermined programming schedule.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Abstract: According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.