Abstract: An audio recognition method, a method of training an audio recognition model, and an electronic device are provided, which relate to fields of artificial intelligence, speech recognition, deep learning and natural language processing technologies. The audio recognition method includes: truncating an audio feature of target audio data to obtain at least one first audio sequence feature corresponding to a predetermined duration; obtaining, according to a peak information of the audio feature, a peak sub-information corresponding to the first audio sequence feature; performing at least one decoding operation on the first audio sequence feature to obtain a recognition result for the first audio sequence feature, a number of times the decoding operation is performed being identical to a number of peaks corresponding to the first audio sequence feature; obtaining target text data for the target audio data according to the recognition result for the at least one first audio sequence feature.

Abstract: Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.

Abstract: A method for training a speech recognition model, a device and a storage medium, which relate to the field of computer technologies, and particularly to the fields of speech recognition technologies, deep learning technologies, or the like, are disclosed. The method for training a speech recognition model includes: obtaining a fusion probability of each of at least one candidate text corresponding to a speech based on an acoustic decoding model and a language model; selecting a preset number of one or more candidate texts based on the fusion probability of each of the at least one candidate text, and determining a predicted text based on the preset number of one or more candidate texts; and obtaining a loss function based on the predicted text and a standard text corresponding to the speech, and training the speech recognition model based on the loss function.

Abstract: The present disclosure provides a method of recognizing speech offline, electronic device, and a storage medium, relating to a field of artificial intelligence such as speech recognition, natural language processing, and deep learning. The method may include: decoding speech data to be recognized into a syllable recognition result; transforming the syllable recognition result into a corresponding text as a speech recognition result of the speech data.

Abstract: A method for mining feature information, an apparatus for mining feature information and an electronic device are disclosed. The method includes: determining a usage scenario of a target device; obtaining raw audio data including real scenario data, speech synthesis data, recorded audio data and other media data; generating target audio data of the usage scenario by simulating the usage scenario based on the raw audio data; and obtaining feature information of the usage scenario by performing feature extraction on the target audio data.

Abstract: The disclosure discloses a method for semantic recognition, an electronic device, and a storage medium. The detailed solution includes: obtaining a speech recognition result of a speech to be processed, in which the speech recognition result includes a newly added recognition result fragment and a historical recognition result fragment; obtaining a semantic vector of each historical object in the historical recognition result fragment, and obtaining a semantic vector of each newly added object by inputting the semantic vector of each historical object and each newly added object in the newly added recognition result fragment into a streaming semantic coding layer; and obtaining a semantic recognition result of the speech by inputting the semantic vector of each historical object and the semantic vector of each newly added object into a streaming semantic vector fusion layer and a semantic understanding multi-task layer sequentially arranged.

Abstract: Proposed are a method and apparatus for speech recognition, and a storage medium. The specific solution includes: obtaining audio data to be recognized; decoding the audio data to obtain a first syllable of a to-be-converted word, in which the first syllable is a combination of at least one phoneme corresponding to the to-be-converted word; obtaining a sentence to which the to-be-converted word belongs and a converted word in the sentence, and obtaining a second syllable of the converted word; encoding the first syllable and the second syllable to generate first encoding information of the first syllable; and decoding the first encoding information to obtain a text corresponding to the to-be-converted word.