Please use this identifier to cite or link to this item:
Title: Frame-based SEMG-to-speech conversion
Authors: Lam, YM
Leong, PHW
Mak, MW 
Keywords: Fourier transforms
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.
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

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Jul 29, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.