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Noisy Speech Recognition by Mel-LPC based AR-HMM with Power and Time Derivative Parameters

by M. Babul Islam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 42
Year of Publication: 2018
Authors: M. Babul Islam
10.5120/ijca2018917149

M. Babul Islam . Noisy Speech Recognition by Mel-LPC based AR-HMM with Power and Time Derivative Parameters. International Journal of Computer Applications. 180, 42 ( May 2018), 1-5. DOI=10.5120/ijca2018917149

@article{ 10.5120/ijca2018917149,
author = { M. Babul Islam },
title = { Noisy Speech Recognition by Mel-LPC based AR-HMM with Power and Time Derivative Parameters },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 42 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number42/29408-2018917149/ },
doi = { 10.5120/ijca2018917149 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:21.476934+05:30
%A M. Babul Islam
%T Noisy Speech Recognition by Mel-LPC based AR-HMM with Power and Time Derivative Parameters
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 42
%P 1-5
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, AR-HMM on mel-scale with power and Mel-LPC based time derivative parameters has been presented for noisy speech recognition. The mel-scaled AR coefficients and melprediction coefficients for Mel-LPC have been calculated on the linear frequency scale from the speech signal without applying bilinear transformation. This has been done by using a first-order allpass filter instead of unit delay. In addition, Mel-Wiener filter has been applied to the system to improve the recognition accuracy in presence of additive noise. The proposed system is evaluated on Aurora 2 database, and the overall recognition accuracy has been found to be 80.02% on the average.

References
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Index Terms

Computer Science
Information Sciences

Keywords

AR-HMM Mel-LPC Mel-Wiener filter Aurora 2 database

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