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AM-FM Based Robust Speaker Identification in Babble Noise

Published on None 2011 by Mangesh S. Deshpande, Raghunath S. Holambe
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 14
None 2011
Authors: Mangesh S. Deshpande, Raghunath S. Holambe
4c466db1-dfa6-4145-a647-aad732a74849

Mangesh S. Deshpande, Raghunath S. Holambe . AM-FM Based Robust Speaker Identification in Babble Noise. International Conference and Workshop on Emerging Trends in Technology. ICWET, 14 (None 2011), 28-35.

@article{
author = { Mangesh S. Deshpande, Raghunath S. Holambe },
title = { AM-FM Based Robust Speaker Identification in Babble Noise },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 14 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 28-35 },
numpages = 8,
url = { /proceedings/icwet/number14/2167-is265/ },
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 Mangesh S. Deshpande
%A Raghunath S. Holambe
%T AM-FM Based Robust Speaker Identification in Babble Noise
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 14
%P 28-35
%D 2011
%I International Journal of Computer Applications
Abstract

Speech babble is one of the most challenging noise interference due to its speaker/speech like characteristics for speech and speaker recognition systems. Performance of such systems strongly degrades in the presence of background noise, like the babble noise. Existing techniques solve this problem by additional processing of speech signal to remove noise. In contrast to existing works, the aim is to improve noise robustness focusing on the features only. To derive robust features, amplitude modulation - frequency modulation (AM-FM) based speaker model is proposed. The robust features are derived by fusing the characteristics of speech production and speech perception mechanisms. The performance is evaluated using clean speech corpus from TIMIT database combined with babble noise from the NOISEX-92 database. Experimental results show that the proposed features significantly improve the performance over the conventional Mel frequency cepstral coefficient (MFCC) features under mismatched training and testing environments.

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Index Terms

Computer Science
Information Sciences

Keywords

Speaker identification AM-FM model Babble noise