  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. In this work, we propose Diff-ETS, an ETS model which
uses a score-based diffusion probabilistic model to enhance the naturalness of
synthesised speech. The diffusion model is applied to improve the quality of
the acoustic features predicted by an EMG encoder. In our experiments, we
evaluated fine-tuning the diffusion model on predictions of a pre-trained EMG
encoder, and training both models in an end-to-end fashion. We compared
Diff-ETS with a baseline ETS model without diffusion using objective metrics
and a listening test. The results indicated the proposed Diff-ETS significantly
improved speech naturalness over the baseline.
