Abstract: This application provides a video message generation method and apparatus, an electronic device, and a storage medium. The video message generation method includes: receiving a comment instruction for a video; determining a target frame corresponding to the comment instruction in the video; obtaining input comment content of the target frame; and generating a video message based on the video, the target frame, and the comment content of the target frame.

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: Embodiments of this application relate to the video coding and compression field, and disclose an encoding method and apparatus, and a decoding method and apparatus, to resolve a problem that an existing split mode cannot satisfy a relatively complex texture requirement. The decoding method specifically includes: parsing a bitstream to determine a basic split mode for a current to-be-decoded picture block and a target derivation mode for a subpicture block of the current to-be-decoded picture block; splitting the current to-be-decoded picture block into N subpicture blocks in the basic split mode, where N is an integer greater than or equal to 2; deriving one derived picture block from at least two adjacent subpicture blocks in the N subpicture blocks in the target derivation mode; and decoding the derived picture block.

Abstract: A file commenting method includes displaying file content in an instant messaging client, displaying comment content entered via a commenting operation performed on a portion of the file content, and displaying a session page when the commenting operation is completed, the session page including the portion of the file content, the comment content, and a file identifier corresponding to the file content.

Abstract: A speech recognition method includes obtaining a speech recognition model including a plurality of feature aggregation nodes connected via a first type operation element, where a context-dependent operation of the first type operation element is based on past speech data and is independent of future speech data. The method further includes receiving streaming speech data, the speech data comprising audio data including speech, and processing the streaming speech data via the speech recognition model to obtain a speech recognition text corresponding to the streaming speech data, and outputting the speech recognition text.

Abstract: A speech decoding method is performed by a computer device, the speech including a current audio frame and a previous audio frame. The method includes: obtaining a target token corresponding to a smallest decoding score from a first token list including first tokens obtained by decoding the previous audio frame, each first token including a state pair and a decoding score, the state pair being used for characterizing a correspondence between a first state of the first token in a first decoding network corresponding to a low-order language model and a second state of the first token in a second decoding network corresponding to a differential language model; determining pruning parameters according to the target token and an acoustic vector of the current audio frame when the current audio frame is decoded; and decoding the current audio frame according to the first token list, the pruning parameters, and the acoustic vector.

Abstract: System and methods for generating user behaviors using a speech recognition method are provided. The method may include obtaining an audio file including speech data associated with one or more speakers and separating the audio file into one or more audio sub-files that each includes a plurality of speech segments. Each of the one or more audio sub-files may correspond to one of the one or more speakers. The method may further include obtaining time information and speaker identification information corresponding to each of the plurality of speech segments and converting the plurality of speech segments to a plurality of text segments. Each of the plurality of speech segments may correspond to one of the plurality of text segments. The method may further include generating first feature information based on the plurality of text segments, the time information, and the speaker identification information.

Abstract: According to one embodiment, an apparatus for applying pitch features in automatic speech recognition is provided. The apparatus includes a distribution evaluation module, normalization module, and random value adjusting module. The distribution evaluation module evaluates the global distribution of pitch features of voiced frames in speech signals, and the global distribution of random values for unvoiced frames in speech signals. The normalization module normalizes the global distribution of random values for unvoiced frames based on the global distribution of pitch features of voiced frames. The random value adjusting module adjusts random values for unvoiced frames based on the normalized global distribution, so that the adjusted random values can be assigned to unvoiced frames in speech signals as pitch features of the unvoiced frames.

Abstract: According to one embodiment, an apparatus for applying pitch features in automatic speech recognition is provided. The apparatus includes a distribution evaluation module, normalization module, and random value adjusting module. The distribution evaluation module evaluates the global distribution of pitch features of voiced frames in speech signals, and the global distribution of random values for unvoiced frames in speech signals. The normalization module normalizes the global distribution of random values for unvoiced frames based on the global distribution of pitch features of voiced frames. The random value adjusting module adjusts random values for unvoiced frames based on the normalized global distribution, so that the adjusted random values can be assigned to unvoiced frames in speech signals as pitch features of the unvoiced frames.