Voiceless Bangla vowel recognition using sEMG signal

Mostafa, S. S., Awal, M. A., Ahmad, M. and Rashid, M. A. (2016) Voiceless Bangla vowel recognition using sEMG signal. SpringerPlus, 5 1: . doi:10.1186/s40064-016-3170-9


Author Mostafa, S. S.
Awal, M. A.
Ahmad, M.
Rashid, M. A.
Title Voiceless Bangla vowel recognition using sEMG signal
Journal name SpringerPlus   Check publisher's open access policy
ISSN 2193-1801
Publication date 2016-12-01
Sub-type Article (original research)
DOI 10.1186/s40064-016-3170-9
Open Access Status DOI
Volume 5
Issue 1
Total pages 15
Place of publication Heidelberg, Germany
Publisher SpringerOpen
Collection year 2017
Language eng
Abstract Some people cannot produce sound although their facial muscles work properly due to having problem in their vocal cords. Therefore, recognition of alphabets as well as sentences uttered by these voiceless people is a complex task. This paper proposes a novel method to solve this problem using non-invasive surface Electromyogram (sEMG). Firstly, eleven Bangla vowels are pronounced and sEMG signals are recorded at the same time. Different features are extracted and mRMR feature selection algorithm is then applied to select prominent feature subset from the large feature vector. After that, these prominent features subset is applied in the Artificial Neural Network for vowel classification. This novel Bangla vowel classification method can offer a significant contribution in voice synthesis as well as in speech communication. The result of this experiment shows an overall accuracy of 82.3 % with fewer features compared to other studies in different languages.
Keyword ANN
Bangla vowel
Classification
Feature selection
SEMG
Wavelet transform
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
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