FURTHER INVESTIGATIONS ON EMG-TO-SPEECH CONVERSION

Our study deals with a Silent Speech Interface based on mapping surface electromyographic (EMG) signals to speech waveforms. Electromyographic signals recorded from the facial muscles capture the activity of the human articulatory apparatus and therefore allow to retrace speech, even when no audible signal is produced. The mapping of EMG signals to speech is done via a Gaussian mixture model (GMM)-based conversion technique. In this paper, we follow the lead of EMG-based speech-to-text systems and apply two major recent technological advances to our system, namely, we consider session-independent systems, which are robust against electrode repositioning, and we show that mapping the EMG signal to whispered speech creates a better speech signal than a mapping to normally spoken speech. We objectively evaluate the performance of our systems using a spectral distortion measure.