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Performance Evaluation of CMN for Mel-LPC based Speech Recognition in Different Noisy Environments

by Md. Mahfuzur Rahman, Sanjit Kumar Saha, Md. Zakir Hossain, Md. Babul Islam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 10
Year of Publication: 2012
Authors: Md. Mahfuzur Rahman, Sanjit Kumar Saha, Md. Zakir Hossain, Md. Babul Islam
10.5120/9316-3548

Md. Mahfuzur Rahman, Sanjit Kumar Saha, Md. Zakir Hossain, Md. Babul Islam . Performance Evaluation of CMN for Mel-LPC based Speech Recognition in Different Noisy Environments. International Journal of Computer Applications. 58, 10 ( November 2012), 6-10. DOI=10.5120/9316-3548

@article{ 10.5120/9316-3548,
author = { Md. Mahfuzur Rahman, Sanjit Kumar Saha, Md. Zakir Hossain, Md. Babul Islam },
title = { Performance Evaluation of CMN for Mel-LPC based Speech Recognition in Different Noisy Environments },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 10 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number10/9316-3548/ },
doi = { 10.5120/9316-3548 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:04.434156+05:30
%A Md. Mahfuzur Rahman
%A Sanjit Kumar Saha
%A Md. Zakir Hossain
%A Md. Babul Islam
%T Performance Evaluation of CMN for Mel-LPC based Speech Recognition in Different Noisy Environments
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 10
%P 6-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study is intended to develop a noise robust distributed speech recognizer for real-world applications by employing Cepstral Mean Normalization (CMN) for robust feature extraction. The main focus of the work is to cope with different noisy environments. To realize this objective, Mel-LP based speech analysis has been used in speech coding on the linear frequency scale by applying a first-order all-pass filter instead of a unit delay. Mismatch between training and test phases is reduced through robust feature extraction by applying CMN on Mel-LP cepstral coefficients as an effort to reduce additive noise and channel distortion. The performance of the proposed system has been evaluated on test set A of Aurora-2 database which is a subset of TIDigits database contaminated by additive noises and channel effects. The experiment is conducted on four different noisy environments and the baseline performance, that is, for Mel-LPC the average word accuracy has found to be 59. 05%. By applying the CMN on Mel-LP cepstral coefficients, the performance has been improved to 68. 02%. It is found that CMN performs significantly better for different noisy environments.

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Index Terms

Computer Science
Information Sciences

Keywords

Mel-LPC bilinear transformation CMN Aurora 2 database

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