Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language

Abstract

Silent speech decoding (SSD), based on articulatory neuromuscular activities, has become a prevalent task of brain–computer interfaces (BCIs) 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-the-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.

Frame-Based Phone Classification Using EMG Signals

“…Technologies to capture these biosignals include vocal tract imaging [14], magnetic tracing [15], electroencephalogram [16], and EMG [17,18]. The conversion from these silent biosignals to audible speech can be done directly-using some machine-learning algorithms that model the relationship between the feature vectors extracted from the biosignals and the acoustic signals [5,19]-or indirectly-by first producing the related text [20][21][22] and then using a text-to-speech (TTS) model to generate synthetic speech.…”