TY - GEN
T1 - A Variable-speed Silent Speech Recognition Method based on Surface Electromyography Signal
AU - Liang, Sui
AU - Xu, Yin
AU - Yuan, Zhaohua
AU - Sun, Lining
AU - Li, Weida
AU - Zhang, Hongmiao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the practical implementation of silent speech recognition based on surface electromyography (sEMG) signal, the change in the subjects' speech speed will affect the recognition performance. To mitigate this effect, a silent speech recognition method based on dynamic time warping (DTW) algorithm is proposed in this paper. Specifically, the facial sEMG signals of 22 Chinese words are collected, the energy threshold method is used to detect the active segment, the root mean square feature is extracted, and finally the DTW algorithm is used for recognition. The experimental results show that the method is robust to silent speech recognition tasks with different speech speeds. The average accuracy of using the DTW algorithm to classify uniform-speed words is 94.55%, and that of variable-speed words is 71.82%. In addition, the proposed method is suitable for few samples learning, which means it can quickly adapt to new tasks and individuals. These findings provide a novel way for the practical application of sEMG based silent speech recognition.
AB - In the practical implementation of silent speech recognition based on surface electromyography (sEMG) signal, the change in the subjects' speech speed will affect the recognition performance. To mitigate this effect, a silent speech recognition method based on dynamic time warping (DTW) algorithm is proposed in this paper. Specifically, the facial sEMG signals of 22 Chinese words are collected, the energy threshold method is used to detect the active segment, the root mean square feature is extracted, and finally the DTW algorithm is used for recognition. The experimental results show that the method is robust to silent speech recognition tasks with different speech speeds. The average accuracy of using the DTW algorithm to classify uniform-speed words is 94.55%, and that of variable-speed words is 71.82%. In addition, the proposed method is suitable for few samples learning, which means it can quickly adapt to new tasks and individuals. These findings provide a novel way for the practical application of sEMG based silent speech recognition.
KW - Surface electromyography (sEMG) signal
KW - dynamic time warping (DTW)
KW - silent speech recognition
KW - variable-speed speech recognition
UR - https://www.scopus.com/pages/publications/85171532244
U2 - 10.1109/ICARM58088.2023.10218782
DO - 10.1109/ICARM58088.2023.10218782
M3 - 会议稿件
AN - SCOPUS:85171532244
T3 - 2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
SP - 798
EP - 802
BT - 2023 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023
Y2 - 8 July 2023 through 10 July 2023
ER -