emg2vec: Self-supervised Pretraining in Electromyography-based Silent Speech Interfaces.

Silent speech interfaces (SSI) enable the generation of audio speech or readable texts without vocalization. Electromyography (EMG), being one of the possible source signals of SSI, demonstrates its superiority, particularly for individuals with vocal organ injuries. In this work, we propose a self-pretraining framework, i.

Diff-ETS: Learning a Diffusion Probabilistic Model for Electromyography-to-Speech Conversion.

Electromyography-to-Speech (ETS) conversion has demonstrated its potential for silent speech interfaces by generating audible speech from Electromyography (EMG) signals during silent articulations. ETS models usually consist of an EMG encoder which converts EMG signals to acoustic speech features, and a vocoder which then synthesises the speech signals. Due to an inadequate amount of available data and noisy signals, the synthesised speech often exhibits a low level of naturalness.

Cross-Speaker Training and Adaptation for Electromyography-to-Speech Conversion.

Surface Electromyography (EMG) signals of articulatory muscles can be used to synthesize acoustic speech with Electromyography-to-Speech (ETS) models. Recent models have improved the synthesis quality by combining training data from multiple recordings of single speakers. In this work, we evaluated whether using recordings of multiple speakers also increases performance and if cross-speaker models can be adapted to unseen speakers with limited data.