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公开(公告)号:US20240346950A1
公开(公告)日:2024-10-17
申请号:US18399891
申请日:2023-12-29
发明人: Jing-Jing GUO , Steve Shu LIU
IPC分类号: G09B19/04 , G10L15/02 , G10L21/0208 , G10L25/51
CPC分类号: G09B19/04 , G10L15/02 , G10L21/0208 , G10L25/51 , G10L2015/025
摘要: A speaking practice system with redundant pronunciation correction is shown, which provides a goodness of pronunciation (GOP) evaluation system running on a data processing server to detect redundant pronunciation in an audio recording. The audio recording is recorded when the user reads a practice text aloud. According to the detected redundant pronunciations, the user is informed to make corrections.
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公开(公告)号:US20240347054A1
公开(公告)日:2024-10-17
申请号:US18399876
申请日:2023-12-29
发明人: Jing-Jing GUO , Steve Shu LIU
IPC分类号: G10L15/187 , G06N3/02 , G10L25/51
CPC分类号: G10L15/187 , G06N3/02 , G10L25/51
摘要: Goodness of pronunciation (GOP) evaluation techniques with improved reliability are presented. A data preprocessing server operates a data pre-processing system and a GOP evaluation system. The data pre-processing system includes a phonetic symbol generation system and an audio recording preprocessing system. Based on a practice text as well as an audio recording of the user reading the practice text, the phonetic symbol generation system generates phonetic symbols, and the audio recording preprocessing system generates audio data. The GOP evaluation system scores the audio recording based on the phonetic symbols and the audio data. The phonetic symbol generation system operates an artificial intelligence model, which generates the phonetic symbols in response to the fact that the practice text includes polyphonic words. Polyphonic words are words with several pronunciations due to their parts of speech, or special words which are numbers or place names.
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公开(公告)号:US20240242710A1
公开(公告)日:2024-07-18
申请号:US18302180
申请日:2023-04-18
发明人: Jiah-Hui LUO , Jing-Jing GUO
IPC分类号: G10L15/065 , G10L15/16 , G10L15/18
CPC分类号: G10L15/065 , G10L15/16 , G10L15/18
摘要: A system for updating language models is provided. The system includes a data-storage module, a data-update module, and a model-building module. The data-storage module is used for storing multiple pieces of corpus data that corresponds to multiple categories. The data-update module is used for storing a piece of new corpus data into the data-storage module. The piece of new corpus data corresponds to one of the categories. The model-building module is used for building a plurality of classified language models, and for updating one of the classified language models based on the piece of new corpus data stored in the data-storage module. The classified language model updated corresponds to the category that corresponds to the piece of new corpus data.
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