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1.
公开(公告)号:US20240221727A1
公开(公告)日:2024-07-04
申请号:US18266432
申请日:2022-09-01
Inventor: Lanhua YOU , Lei JIA , Qi ZHANG , Zhengxiang JIANG
CPC classification number: G10L15/063 , G10L15/01 , G10L15/02 , G10L15/16
Abstract: The present disclosure provides a voice recognition model training method and apparatus, an electronic device and a storage medium, relating to the field of artificial intelligence technology, and in particular to the fields such as deep learning and voice recognition. The specific implementation scheme includes constructing a negative sample according to a positive sample to obtain a target negative sample for constraining a voice decoding path; obtaining training data according to the positive sample and the target negative sample; and training a first voice recognition model according to the training data to obtain a second voice recognition model.
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2.
公开(公告)号:US20220139369A1
公开(公告)日:2022-05-05
申请号:US17530276
申请日:2021-11-18
Inventor: Zhijian WANG , Sheng QIAN , Qi ZHANG
IPC: G10L15/00 , G10L15/183 , G10L15/32
Abstract: A method for recognizing a Chinese-English mixed speech, includes: determining pronunciation information and scores of a language model, of speech information, in response to receiving the speech information; determining whether an English word exists in content of the speech information based on the pronunciation information; determining a Chinese word corresponding to the English word based on a preset Chinese-English mapping table in response to the English word existing in the content of the speech information, in which the Chinese-English mapping table includes a mapping relationship of at least one pair of English word and Chinese word; determining a score of the Chinese word corresponding to the English word; replacing a score of the English word in the scores of the language model with the score of the Chinese word; and obtaining a speech recognition result for the speech information based on the replaced scores of the language model.
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公开(公告)号:US20220245764A1
公开(公告)日:2022-08-04
申请号:US17723201
申请日:2022-04-18
Inventor: Qi ZHANG , Kangyi ZHI
Abstract: A method for an image super-solution, a device and a storage medium are provided. The method may include: acquiring training samples, where the training samples include a first-resolution sample image and a corresponding second-resolution sample image, and a resolution of the second-resolution sample image is N times a resolution of the sample of first resolution, N being a positive integer; and training an initial network model by using the first-resolution sample image as an input and using the second-resolution sample image as an output, to obtain the super-resolution model.
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公开(公告)号:US20220108684A1
公开(公告)日:2022-04-07
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:US20230011823A1
公开(公告)日:2023-01-12
申请号:US17939401
申请日:2022-09-07
Inventor: Qi ZHANG , Weidong Hu
Abstract: The present disclosure provides a method and apparatus for converting an image format, an electronic device, a computer readable storage medium and a computer program product, relates to the field of artificial intelligence technology such as computer vision and deep learning, and can be applied to intelligent sensing ultra-definition scenarios. A specific implementation of the method includes: acquiring a to-be-converted standard dynamic range image; performing a convolution operation on the standard dynamic range image to obtain a local feature; performing a global average pooling operation on the standard dynamic range image to obtain a global feature; and converting the standard dynamic range image into a high dynamic range image according to the local feature and the global feature.
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