公开(公告)号：US12183324B2

公开(公告)日：2024-12-31

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 ,  G06F16/683 ,  G10L15/187 ,  G10L15/26

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.

公开(公告)号：US20220207427A1

公开(公告)日：2022-06-30

申请号：US17655253

申请日：2022-03-17

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

Inventor： Yangkai Xu ,  Guibin Wang ,  Xiaoyin Fu ,  Zhijie Chen ,  Mingshun Yang ,  Shijun Cong ,  Ming Jia ,  Lei Jia

IPC: G06N20/00

Abstract: A method for training a data processing model includes: acquiring sample data; acquiring an initial data processing model, the initial data processing model including a plurality of forward nodes for outputting a plurality of intermediate results corresponding to the sample data; determining a plurality of time-dependent features corresponding to the plurality of forward nodes; acquiring a data processing model to be trained by processing the initial data processing model based on the plurality of time-dependent features; and training the data processing model to be trained using the sample data and the plurality of intermediate results.