Speech Synthesis from Surface Electromyogram Signals

Although speech is the most natural means for communication among humans, there are situations in which speech is impossible or inappropriate. Examples include people with vocal cord damage, underwater communications or in noisy environments. To address some of the limitations of speech communication, nonacoustic communication systems using surface electromyogram signals have been proposed. However, most of the proposed techniques focus on recognizing or classifying the SEMG signals into a limited set of words. This approach shares similarities with isolated word recognition systems in that periods of silence between words are mandatory and they have difficulties in recognizing untrained words and continuous speech. A method for synthesizing speech from surface electromyogram (SEMG) signals in a frame-by-frame basis is presented. The input SEMG signals of spoken