An optimized heuristic domain adaptive structure for EMG-based cross-subject silent speech recognition

Qiang Cui; Xingyu Zhang; Yakun Zhang; Changyan Zheng; Liang Xie; Ye Yan; Erwei Yin

doi:10.1117/12.3033633

13 June 2024 An optimized heuristic domain adaptive structure for EMG-based cross-subject silent speech recognition

Qiang Cui, Xingyu Zhang, Yakun Zhang, Changyan Zheng, Liang Xie, Ye Yan, Erwei Yin

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131805J (2024) https://doi.org/10.1117/12.3033633
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

EMG-based silent speech recognition has achieved favorable performance recently, providing the possibility of assisted therapy for speech disorders and communication in specialized settings. Aiming at handling the dramatic degradation of classification accuracy caused by biological variations and articulation bias across speakers, we propose an optimized heuristic domain adaptive architecture extracting global features, speaker-related domain-specific features, and speakerindependent domain-invariant features from myoelectric signals, respectively. Through separate alignment and optimization in parallel, the effectiveness of the extracted features and the classification performance have been significantly enhanced. Experimental results show that our method achieves a classification accuracy of 94.40%, exceeding the state-of-the-art model by almost 10 percent, in a scenario with 60 subjects for training and 10 new subjects for validation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Cui, Xingyu Zhang, Yakun Zhang, Changyan Zheng, Liang Xie, Ye Yan, and Erwei Yin "An optimized heuristic domain adaptive structure for EMG-based cross-subject silent speech recognition", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131805J (13 June 2024); https://doi.org/10.1117/12.3033633

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