Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21664
Title: Frame-based SEMG-to-speech conversion
Authors: Lam, YM
Leong, PHW
Mak, MW 
Keywords: Fourier transforms
Electromyography
Feature extraction
Medical signal processing
Neural nets
Speech synthesis
Vector quantisation
Issue Date: 2006
Publisher: IEEE
Source: 49th IEEE International Midwest Symposium on Circuits and Systems, 2006 : MWSCAS '06, 6-9 August 2006, San Juan, p. 240-244 How to cite?
Abstract: This paper presents a methodology that uses surface electromyogram (SEMG) signals recorded from the cheek and chin to synthesize speech. A neural network is trained to map the SEMG features (short-time Fourier transform coefficients) into vector-quantized codebook indices of speech features (linear prediction coefficients, pitch, and energy). To synthesize a word, SEMG signals recorded during pronouncing a word are blocked into frames; SEMG features are then extracted from each SEMG frame and presented to the neural network to obtain a sequence of speech feature indices. The waveform of the word is then constructed by concatenating the pre-recorded speech segments corresponding to the feature indices. Experimental evaluations based on the synthesis of eight words show that on average over 70% of the words can be synthesized correctly and the neural network can classify SEMG frames into seven phonemes and silence at a rate of 77.8%. The rate can be further improved to 88.3% by assuming medium-time stationarity of the speech signals. The experimental results demonstrate the feasibility of synthesizing words based on SEMG signals only.
URI: http://hdl.handle.net/10397/21664
ISBN: 1-4244-0172-0
1-4244-0173-9 (E-ISBN)
ISSN: 1548-3746
DOI: 10.1109/MWSCAS.2006.382042
Appears in Collections:Conference Paper

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