Encoder-Decoder Architectures for Silent Speech Recognition based on High-density Surface Electromyogram - Chen, Xi; Zhang, Xu; Chen, Xiang; Chen, Xun (2022 International Conference on Advanced Robotics and Mechatronics (ICARM), 2022)
Silent speech based on surface electromyogram(sEMG) has become an important interaction method. However, existing systems are highly dependent on time-alignment data, which is not conducive to the wide application of silent speech recognition (SSR) systems. In this study, we propose a convolutional Long Short-Term Memory-based encoder-decoder architecture to characterize and decode silent speech without time-alignment training data. The encoder can map the sEMG feature maps into a fixed-length feature vector, and the decoder can decode this vector back to the target sequence. To verify the effectiveness of the proposed method, the experimental data of 33 utterances from 7 subjects were collected from high-density electrode arrays with 64 channels from face and neck muscles. The performance of the proposed method was superior to the benchmark LSTM-based encoder-decoder architecture both on word error rate and utterance classification accuracy. These findings of this work indicate that the proposed method has the potential to achieve a rapid establishment of SSR system.