Session independent non-audible speech recognition using surface electromyography

In this paper we introduce a speech recognition system based on myoelectric signals. The system handles audible and non-audible speech. Major challenges in surface electromyography based speech recognition ensue from repositioning electrodes between recording sessions, environmental temperature changes, and skin tissue properties of the speaker. In order to reduce the impact of these factors, we investigate a variety of signal normalization and model adaptation methods. An average word accuracy of 97.3% is achieved using seven EMG channels and the same electrode positions. The performance drops to 76.2% after repositioning the electrodes if no normalization or adaptation is performed. By applying our adaptation methods we manage to restore the recognition rates to 87.1%. Furthermore, we compare audibly to non-audibly spoken speech. The results suggest that large differences exist between the corresponding muscle movements. Still, our recognition system recognizes both speech manners accurately when trained on pooled data