Direct conversion from facial myoelectric signals to speech using Deep Neural Networks

This paper presents our first results using Deep Neural Networks for surface electromyographic (EMG) speech synthesis. The proposed approach enables a direct mapping from EMG signals captured from the articulatory muscle movements to the acoustic speech signal. Features are processed from multiple EMG channels and are fed into a feed forward neural network to achieve a mapping to the target acoustic speech output. We show that this approach is feasible to generate speech output from the input EMG signal and compare the results to a prior mapping technique based on Gaussian mixture models. The comparison is conducted via objective Mel-Cepstral distortion scores and subjective listening test evaluations. It shows that the proposed Deep Neural Network approach gives substantial improvements for both evaluation criteria.