Beijing Baidu Netcom Science Technology Co., Ltd

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology Co., Ltd.

Xiaoyin Fu

Mingxin Liang

Zhijie Chen

Zhengxiang Jiang

Liao Zhang

Qiguang Zang

Lei Jia

Qi Zhang

Junyao Shao

Haifeng Wang

Wei Wei

Xu Chen

Sheng Qian

Zhen Wu

Jinfeng Bai

Ming Wen

Jiaxiang Ge

Yinlou Zhao

Huanxin Zheng

Mingshun Yang

Maoren Zhou

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.

Method of recognizing speech offline, electronic device, and storage medium

The present disclosure provides speech recognition and codec methods and apparatuses, an electronic device and a storage medium, and relates to the field of artificial intelligence such as intelligent speech, deep learning and natural language processing. The speech recognition method may include: acquiring an audio feature of to-be-recognized speech; encoding the audio feature to obtain an encoding feature; truncating the encoding feature to obtain continuous N feature fragments, N being a positive integer greater than one; and acquiring, for any one of the feature segments, corresponding historical feature abstraction information, encoding the feature segment in combination with the historical feature abstraction information, and decoding an encoding result to obtain a recognition result corresponding to the feature segment, wherein the historical feature abstraction information is information obtained by feature abstraction of recognized historical feature fragments.

Speech recognition and codec method and apparatus, electronic device and storage medium

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.

Method and apparatus for mining feature information, and electronic device

The present disclosure discloses a speech recognition method and apparatus, and relates to the field of speech and deep learning technologies. A specific implementation scheme involves: acquiring candidate recognition results with first N recognition scores outputted by a speech recognition model for to-be-recognized speech, N being a positive integer greater than 1; scoring the N candidate recognition results based on pronunciation similarities between candidate recognition results and pre-collected popular entities, to obtain similarity scores of the candidate recognition results; and integrating the recognition scores and the similarity scores of the candidate recognition results to determine a recognition result corresponding to the to-be-recognized speech from the N candidate recognition results. The present disclosure can improve recognition accuracy.

Speech recognition method and apparatus

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.

Method for training speech recognition model, device and storage medium

The present disclosure provides a method for training a linguistic model, related to fields of speech, natural language processing, deep learning technologies. A method includes: obtaining grammars corresponding to a plurality of sample texts and a slot value of a slot in each grammar by using semantic analysis; generating a grammar graph corresponding to each grammar based on the corresponding grammar and the slot value of the slot in the corresponding grammar; obtaining a weight of each grammar, a weight of each slot, and a weight of each slot value in each grammar graph based on the sample texts; determining at least one grammar frequency of each order based on the weight of each grammar, the weight of each slot, and the weight of each slot value in each grammar graph; and training the linguistic model based on the at least one grammar frequency of each order.

Method for training a linguistic model and electronic device

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.

Method and apparatus for speech recognition, and storage medium

The disclosure provides a method, a device and a storage medium for predicting a punctuation in a text. The method includes: inputting a text to be predicted into a sequence tagging model to obtain at least one prediction result and a corresponding first score of each character in the text to be predicted; generating a text to be inputted corresponding to each of the at least one prediction result; obtaining a second score corresponding to each of the at least one prediction result; determining a punctuation existence situation of the corresponding character based on the first score and the second score corresponding to each of the at least one prediction result; and performing punctuation processing on the text to be predicted based on the punctuation existence situation of each character in the text to be predicted to obtain a punctuated text corresponding to the text to be predicted.

Method, device and storage medium for predicting punctuation in text

Methods, apparatuses, devices and computer-readable storage media for real-time speech recognition are provided. The method includes: based on an input speech signal, obtaining truncating information for truncating a sequence of features of the speech signal; based on the truncating information, truncating the sequence of features into a plurality of subsequences; and for each subsequence in the plurality of subsequences, obtaining a real-time recognition result through attention mechanism.

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Beijing, China

Xiaoyin Fu