Abstract
This paper presents a technique to synthesize speech from SEMG signals using a frame-by- frame basis. SEMG signals are firstly enframed and classified into a number of phonetic classes by a neural network, then the produced sequences of phonetic indices are translated to acoustic signals by concatenating their corresponding pre-recored speech segments. A significant advantage of the proposed synthesis based approach compared with previous recognition based approach is that, human is intelligent enough to recognition the synthesized speech although there is errors in it. Experimental evaluations based on the synthesis of eight words show that on average over 73.4% of the words can be synthesized correctly and the neural network can classify the SEMG frames of seven phonemes at a rate of 81.9%. The accuracy can be increased to 88.6% by using a glitch removal technique to smooth the produced sequence of phonetic indices. The results show that the phoneme-frame based speech synthesis technique can be applied to SEMG-based non-acoustic communication.
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