Abstract: The present disclosure relates to a video interaction method and apparatus, and a device and a medium. The video interaction method comprises: presenting a video list in a video presentation page; when a first operation on the video list is detected, playing, on the video presentation page and by means of a floating window, a video corresponding to the first operation; and when a second operation on the floating window is detected, jumping, from the video presentation page, to display a target video detail page, wherein the target video detail page is a video detail page corresponding to the target video, which is played in the floating window.

Abstract: A system processes audio data to detect when it includes a representation of a wakeword or of an acoustic event. The system may receive or determine acoustic features for the audio data, such as log-filterbank energy (LFBE). The acoustic features may be used by a first, wakeword-detection model to detect the wakeword; the output of this model may be further processed using a softmax function, to smooth it, and to detect spikes. The same acoustic features may be also be used by a second, acoustic-event-detection model to detect the acoustic event; the output of this model may be further processed using a sigmoid function and a classifier. Another model may be used to extract additional features from the LFBE data; these additional features may be used by the other models.

Abstract: A system and method performs wakeword detection and automatic speech recognition using the same acoustic model. A mapping engine maps phones/senones output by the acoustic model to phones/senones corresponding to the wakeword. A hidden Markov model (HMM) may determine that the wakeword is present in audio data; the HMM may have multiple paths for multiple wakewords or may have multiple models. Once the wakeword is detected, ASR is performed using the acoustic model.

Abstract: A system and method performs multilingual wakeword detection by determining a language corresponding to the wakeword. A first wakeword-detection component, which may execute using a digital-signal processor, determines that audio data includes a representation of the wakeword and determines a language corresponding to the wakeword. A second, more accurate wakeword-detection component may then process the audio data using the language to confirm that it includes the representation of the wakeword. The audio data may then be sent to a remote system for further processing.

Abstract: A system processes audio data to detect when it includes a representation of a wakeword or of an acoustic event. The system may receive or determine acoustic features for the audio data, such as log-filterbank energy (LFBE). The acoustic features may be used by a first, wakeword-detection model to detect the wakeword; the output of this model may be further processed using a softmax function, to smooth it, and to detect spikes. The same acoustic features may be also be used by a second, acoustic-event-detection model to detect the acoustic event; the output of this model may be further processed using a sigmoid function and a classifier. Another model may be used to extract additional features from the LFBE data; these additional features may be used by the other models.

Abstract: A system processes audio data to detect when it includes a representation of a wakeword or of an acoustic event. The system may receive or determine acoustic features for the audio data, such as log-filterbank energy (LFBE). The acoustic features may be used by a first, wakeword-detection model to detect the wakeword; the output of this model may be further processed using a softmax function, to smooth it, and to detect spikes. The same acoustic features may be also be used by a second, acoustic-event-detection model to detect the acoustic event; the output of this model may be further processed using a sigmoid function and a classifier. Another model may be used to extract additional features from the LFBE data; these additional features may be used by the other models.

Abstract: A system and method performs multilingual wakeword detection by determining a language corresponding to the wakeword. A first wakeword-detection component, which may execute using a digital-signal processor, determines that audio data includes a representation of the wakeword and determines a language corresponding to the wakeword. A second, more accurate wakeword-detection component may then process the audio data using the language to confirm that it includes the representation of the wakeword. The audio data may then be sent to a remote system for further processing.

Abstract: A system processes audio data to detect when it includes a representation of a wakeword or of an acoustic event. The system may receive or determine acoustic features for the audio data, such as log-filterbank energy (LFBE). The acoustic features may be used by a first, wakeword-detection model to detect the wakeword; the output of this model may be further processed using a softmax function, to smooth it, and to detect spikes. The same acoustic features may be also be used by a second, acoustic-event-detection model to detect the acoustic event; the output of this model may be further processed using a sigmoid function and a classifier. Another model may be used to extract additional features from the LFBE data; these additional features may be used by the other models.

Abstract: Embodiments of the present invention provide a method and an apparatus for word detection in an application program. The method includes extracting a resource file from a multilingual application program installation package and converting the resource file into a text file. The method further includes disassembling the text file according to a language version to acquire a corresponding language text file; invoking a language detection tool according to the language version; and checking the language text file by using the language detection tool to identify questionable character information. The apparatus for word detection includes a file processing module, configured to extract a resource file from a multilingual application program installation package, and convert the resource file into a text file; and a disassembling module, configured to disassemble the text file according to a language version to acquire a corresponding language text file.

Abstract: A method for selective transmission of audio data to a speech processing server uses detection of an acoustic trigger in the audio data in determining the data to transmit. Detection of the acoustic trigger makes use of an efficient computation approach that reduces the amount of run-time computation required, or equivalently improves accuracy for a given amount of computation, by using a neural network to determine an indicator of presence of the acoustic trigger. In some example, the neural network combines a “time delay” structure in which intermediate results of computations are reused at various time delays, thereby avoiding computation of computing new results, and decomposition of certain transformations to require fewer arithmetic operations without sacrificing significant performance.

Abstract: A method for selective transmission of audio data to a speech processing server uses detection of an acoustic trigger in the audio data in determining the data to transmit. Detection of the acoustic trigger makes use of an efficient computation approach that reduces the amount of run-time computation required, or equivalently improves accuracy for a given amount of computation, by combining a “time delay” structure in which intermediate results of computations are reused at various time delays, thereby avoiding computation of computing new results, and decomposition of certain transformations to require fewer arithmetic operations without sacrificing significant performance. For a given amount of computation capacity the combination of these two techniques provides improved accuracy as compared to current approaches.

Abstract: Embodiments of the present invention provide a method and an apparatus for word detection in an application program. The method includes extracting a resource file from a multilingual application program installation package and converting the resource file into a text file. The method further includes disassembling the text file according to a language version to acquire a corresponding language text file; invoking a language detection tool according to the language version; and checking the language text file by using the language detection tool to identify questionable character information. The apparatus for word detection includes a file processing module, configured to extract a resource file from a multilingual application program installation package, and convert the resource file into a text file; and a disassembling module, configured to disassemble the text file according to a language version to acquire a corresponding language text file.

Abstract: Embodiments of the present invention provide a method and an apparatus for word detection in an application program. The method includes extracting a resource file from a multilingual application program installation package and converting the resource file into a text file. The method further includes disassembling the text file according to a language version to acquire a corresponding language text file; invoking a language detection tool according to the language version; and checking the language text file by using the language detection tool to identify questionable character information. The apparatus for word detection includes a file processing module, configured to extract a resource file from a multilingual application program installation package, and convert the resource file into a text file; and a disassembling module, configured to disassemble the text file according to a language version to acquire a corresponding language text file.

Abstract: Embodiments of the present invention provide a method and an apparatus for word detection in an application program. The method includes extracting a resource file from a multilingual application program installation package and converting the resource file into a text file. The method further includes disassembling the text file according to a language version to acquire a corresponding language text file; invoking a language detection tool according to the language version; and checking the language text file by using the language detection tool to identify questionable character information. The apparatus for word detection includes a file processing module, configured to extract a resource file from a multilingual application program installation package, and convert the resource file into a text file; and a disassembling module, configured to disassemble the text file according to a language version to acquire a corresponding language text file.

Abstract: A computer-implemented method of analyzing a sample task trajectory including obtaining, with one or more computers, position information of an instrument in the sample task trajectory, obtaining, with the one or more computers, pose information of the instrument in the sample task trajectory, comparing, with the one or more computers, the position information and the pose information for the sample task trajectory with reference position information and reference pose information of the instrument for a reference task trajectory, determining, with the one or more computers, a skill assessment for the sample task trajectory based on the comparison, and outputting, with the one or more computers, the determined skill assessment for the sample task trajectory.