公开(公告)号：US20220068265A1

公开(公告)日：2022-03-03

申请号：US17521473

申请日：2021-11-08

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

Inventor： Junyao SHAO ,  Sheng QIAN

IPC: G10L15/16 ,  G10L15/04 ,  G10L15/02 ,  G10L15/22

Abstract: The disclosure discloses a method for displaying a streaming speech recognition result, relates to a field of speech technologies, deep learning technologies and natural language processing technologies. The method includes: obtaining a plurality of continuous speech segments of an input audio stream, and simulating an end of a target speech segment in the plurality of continuous speech segments as a sentence ending, performing feature extraction on a current speech segment to be recognized based on a first feature extraction mode when the current speech segment is the target speech segment; performing feature extraction on the current speech segment based on a second feature extraction mode when the current speech segment is not the target speech segment; and obtaining a real-time recognition result by inputting a feature sequence extracted from the current speech segment into a streaming multi-layer truncated attention model, and displaying the real-time recognition result.

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.