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Decoding silent speech from high-density surface electromyographic data using transformer

文献类型：期刊论文

作者Song, Rui3; Zhang, Xu3; Chen, Xi3; Chen, Xiang3; Chen, Xun3; Yang, Shuang2; Yin, Erwei1
刊名BIOMEDICAL SIGNAL PROCESSING AND CONTROL
出版日期2023-02-01
卷号80页码:9
关键词Surface electromyography Silent speech recognition Sequential decoding Transformer Language model
ISSN号1746-8094
DOI10.1016/j.bspc.2022.104298
英文摘要Recent silent speech recognition (SSR) studies based on surface electromyography (sEMG) have been conducted by classifying a finite number of words or phrases without sufficient understanding of temporally semantic in-formation compared to sequential decoding at a fine-grained syllable or phoneme level. This paper presents a syllable-level sequential decoding method using a transformer model for sEMG-based SSR. The proposed method consists of a transformer model and a language model. The input sEMG data was first translated into a sequence of syllable-level decisions by the transformer model. Then, these sequential syllable-level decisions were tuned as a final syllable sequence to approximate natural language through the language model. To verify the effec-tiveness of the proposed method, experiment data were recorded using two high-density electrode arrays with 64 channels from a total of eight subjects during subvocally reading a corpus of 33 Chinese phrases generated from a dictionary of 82 syllables. The proposed method achieved the lowest character error rate of 5.14 +/- 3.28 % and the highest phrase recognition accuracy of 96.37 +/- 2.06 %, and it significantly outperformed other common methods for sEMG-based SSR. These findings demonstrated the feasibility and usability of the proposed method for practical SSR applications.
资助项目National Natural Science Foundation of China[62271464] ; National Natural Science Foundation of China[62076250]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000890503700003
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/20269]  
专题中国科学院计算技术研究所期刊论文
通讯作者Zhang, Xu
作者单位1.Acad Mil Sci Peoples Liberat Army, Natl Innovat Inst Def Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Technol Univ Sci & Technol China, Sch Informat Sci, Hefei, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Song, Rui,Zhang, Xu,Chen, Xi,et al. Decoding silent speech from high-density surface electromyographic data using transformer[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,80:9.
APA Song, Rui.,Zhang, Xu.,Chen, Xi.,Chen, Xiang.,Chen, Xun.,...&Yin, Erwei.(2023).Decoding silent speech from high-density surface electromyographic data using transformer.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,80,9.
MLA Song, Rui,et al."Decoding silent speech from high-density surface electromyographic data using transformer".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 80(2023):9.

入库方式： OAI收割

来源：计算技术研究所

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