Impact of lack of acoustic feedback in EMG-based silent speech recognition

This paper presents our recent advances in speech recognition based on surface electromyography (EMG). This technology allows for Silent Speech Interfaces since EMG captures the electrical potentials of the human articulatory muscles rather than the acoustic speech signal. Our earlier experiments have shown that the EMG signal is greatly impacted by the mode of speaking. In this study we extend this line of research by comparing EMG signals from audible, whispered, and silent speaking mode. We distinguish between phonetic features like consonants and vowels and show that the lack of acoustic feedback in silent speech implies an increased focus on somatosensoric feedback, which is visible in the EMG signal. Based on this analysis we develop a spectral mapping method to compensate for these differences. Finally, we apply the spectral mapping to the front-end of our speech recognition system and show that recognition rates on silent speech improve by up to 11.59% relative.