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Article:Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC

by Satyanand Singh, Dr. E.G. Rajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 1
Year of Publication: 2011
Authors: Satyanand Singh, Dr. E.G. Rajan
10.5120/2188-2774

Satyanand Singh, Dr. E.G. Rajan . Article:Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC. International Journal of Computer Applications. 17, 1 ( March 2011), 1-7. DOI=10.5120/2188-2774

@article{ 10.5120/2188-2774,
author = { Satyanand Singh, Dr. E.G. Rajan },
title = { Article:Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 1 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number1/2188-2774/ },
doi = { 10.5120/2188-2774 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:28.308329+05:30
%A Satyanand Singh
%A Dr. E.G. Rajan
%T Article:Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 1
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is strongly determined by the quality of the front-end. In other words, classification can be at most as accurate as the features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. In this paper it has been shown that the inverted Mel-Frequency Cepstral Coefficients is one of the performance enhancement parameters for speaker recognition, which contains high frequency region complementary information in it. This paper introduces the Gaussian shaped filter (GF) while calculation MFCC and inverted MFCC in place of traditional triangular shaped bins. The main idea is to introduce a higher amount of correlation between subband outputs. The performance of both MFCC and inverted MFCC improve with GF over traditional triangular filter (TF) based implementation, individually as well as in combination. In this study the Vector Quantization (VQ) feature matching technique was used, due to high accuracy and its simplicity. The proposed investigation achieved 98.57% of efficiency with a very short test voice sample 2 seconds.

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Index Terms

Computer Science
Information Sciences

Keywords

GF Triangular Filter Subbands Correlation MFCC inverted MFCC Vector Quantization

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