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公开(公告)号：US12183323B2
公开(公告)日：2024-12-31
申请号：US17644749
申请日：2021-12-16
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.
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公开(公告)号：US12033616B2
公开(公告)日：2024-07-09
申请号：US17571805
申请日：2022-01-10
Inventor： Junyao Shao , Xiaoyin Fu , Qiguang Zang , Zhijie Chen , Mingxin Liang , Huanxin Zheng , Sheng Qian
IPC: G10L15/06 , G10L15/16 , G10L15/183 , G10L15/28
CPC classification number: G10L15/063 , G10L15/16 , G10L15/183 , 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.
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公开(公告)号：US20220207427A1
公开(公告)日：2022-06-30
申请号：US17655253
申请日：2022-03-17
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.
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公开(公告)号：US12183324B2
公开(公告)日：2024-12-31
申请号：US17738651
申请日：2022-05-06
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.
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