Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language
This work addresses the problem of silent speech decoding for tonal language users, representing an incremental improvement in brain-computer interface applications.
The paper tackles the challenge of restoring silent speech in tonal languages like Mandarin Chinese by proposing an optimized Sequence-to-Sequence approach that synthesizes voice from sEMG-based silent speech, achieving an average character error rate of 6.41% in experiments with six speakers.
Silent Speech Decoding (SSD), based on articulatory neuromuscular activities, has become a prevalent task of Brain-Computer Interface (BCI) in recent years. Many works have been devoted to decoding surface electromyography (sEMG) from articulatory neuromuscular activities. However, restoring silent speech in tonal languages such as Mandarin Chinese is still difficult. This paper proposes an optimized Sequence-to-Sequence (Seq2Seq) approach to synthesize voice from the sEMG-based silent speech. We extract duration information to regulate the sEMG-based silent speech using the audio length. Then, we provide a deep-learning model with an encoder-decoder structure and a state-of-art vocoder to generate the audio waveform. Experiments based on six Mandarin Chinese speakers demonstrate that the proposed model can successfully decode silent speech in Mandarin Chinese and achieve a character error rate (CER) of 6.41% on average with human evaluation.