公开(公告)号：US20230410794A1

公开(公告)日：2023-12-21

申请号：US18237976

申请日：2023-08-25

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Xiaoyin FU ,  Mingshun YANG ,  Qiguang ZANG ,  Zhijie CHEN ,  Yangkai XU ,  Guibin WANG ,  Lei JIA

IPC: G10L15/06 ,  G10L15/02 ,  G10L15/26

CPC classification number: G10L15/063 ,  G10L15/26 ,  G10L15/02

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.

公开(公告)号：US20220108684A1

公开(公告)日：2022-04-07

申请号：US17644749

申请日：2021-12-16

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Xiaoyin FU ,  Mingxin LIANG ,  Zhijie CHEN ,  Qiguang ZANG ,  Zhengxiang JIANG ,  Liao ZHANG ,  Qi ZHANG ,  Lei JIA

IPC: G10L15/02 ,  G10L15/16 ,  G10L19/032

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.

公开(公告)号：US20220310064A1

公开(公告)日：2022-09-29

申请号：US17571805

申请日：2022-01-10

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Junyao SHAO ,  Xiaoyin FU ,  Qiguang ZANG ,  Zhijie CHEN ,  Mingxin LIANG ,  Huanxin ZHENG ,  Sheng QIAN

IPC: G10L15/06 ,  G10L15/183 ,  G10L15/16 ,  G10L15/28

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.

公开(公告)号：US20230090590A1

公开(公告)日：2023-03-23

申请号：US17738651

申请日：2022-05-06

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Xiaoyin FU ,  Zhijie CHEN ,  Mingxin LIANG ,  Mingshun YANG ,  Lei JIA ,  Haifeng WANG

IPC: G10L15/02 ,  G10L15/26 ,  G10L15/187 ,  G06F16/683

Abstract: 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.