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Reseach Article

Speech Recognition using Energy and Zero Crossing Features with Kekre�s Proportionate Error Algorithm

Published on None 2011 by H.B.Kekre, A A Athawale, G J Sharma
journal_cover_thumbnail
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 4
None 2011
Authors: H.B.Kekre, A A Athawale, G J Sharma
d107fa7c-d96b-4102-8774-55522b321c9a

H.B.Kekre, A A Athawale, G J Sharma . Speech Recognition using Energy and Zero Crossing Features with Kekre�s Proportionate Error Algorithm. International Conference and Workshop on Emerging Trends in Technology. ICWET, 4 (None 2011), 33-37.

@article{
author = { H.B.Kekre, A A Athawale, G J Sharma },
title = { Speech Recognition using Energy and Zero Crossing Features with Kekre�s Proportionate Error Algorithm },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 33-37 },
numpages = 5,
url = { /proceedings/icwet/number4/2095-algo487/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A H.B.Kekre
%A A A Athawale
%A G J Sharma
%T Speech Recognition using Energy and Zero Crossing Features with Kekre�s Proportionate Error Algorithm
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 4
%P 33-37
%D 2011
%I International Journal of Computer Applications
Abstract

This paper presents a novel method for isolated English word recognition based on energy and zero crossing features with vector quantization. This isolated word recognition method consists of two phases, feature extraction phase and recognition phase. In feature extraction, end points are detected and noise is removed using end point detection algorithm, a feature vector is obtained by combining the energy and zero cross rate into a different feature vector dimensions of 8, 10 and 12. Recognition phase consists of two steps, feature training and testing, in feature training, codebooks for each reference samples are generated using LBG and KPE Vector Quantization algorithm. For testing Euclidean distance is calculated between test sample feature vector and codebook of all reference speech samples. Speech sample with minimum average distance is selected. Experimental results showed that the maximum recognition rate of 82% is obtained for KPE with codebook size of 64.

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Index Terms

Computer Science
Information Sciences

Keywords

Isolated Speech Recognition Vector Quantization Code vector Codebook Euclidean Distance