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Improving Speaker Identification Performance by Combining Vocal Tract Features

by S. Selva Nidhyananthan, R. Shantha Selva Kumari, G. Jaffino
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 1
Year of Publication: 2012
Authors: S. Selva Nidhyananthan, R. Shantha Selva Kumari, G. Jaffino
http:/ijais12-450433

S. Selva Nidhyananthan, R. Shantha Selva Kumari, G. Jaffino . Improving Speaker Identification Performance by Combining Vocal Tract Features. International Journal of Applied Information Systems. 3, 1 ( July 2012), 27-33. DOI=http:/ijais12-450433

@article{ http:/ijais12-450433,
author = { S. Selva Nidhyananthan, R. Shantha Selva Kumari, G. Jaffino },
title = { Improving Speaker Identification Performance by Combining Vocal Tract Features },
journal = { International Journal of Applied Information Systems },
issue_date = { July 2012 },
volume = { 3 },
number = { 1 },
month = { July },
year = { 2012 },
issn = { 2249-0868 },
pages = { 27-33 },
numpages = {9},
url = { https://www.ijais.org/archives/volume3/number1/197-0433/ },
doi = { http:/ijais12-450433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:45:17.835907+05:30
%A S. Selva Nidhyananthan
%A R. Shantha Selva Kumari
%A G. Jaffino
%T Improving Speaker Identification Performance by Combining Vocal Tract Features
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 3
%N 1
%P 27-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes fusion and addition techniques of vocal tract features such as Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Mel Frequency Cepstral Coefficients (DMFCC) in speaker identification. Feature extraction plays an important role as a front end processing block in Speaker Identification (SI) process. Mel frequency features are used to extract the spectral characteristics of the speech such as formant frequency and the bandwidth of formant frequency. This feature estimation method leads to robust recognition performance. The Dynamic Mel frequency features are used to extract the dynamic behavior of the human vocal tract using pitch frequency. This work is focused to increase the identification accuracy with databases containing short length speech signal. Experimental evaluation is carried out on TIMIT database with 630 speakers using Gaussian Mixture Model (GMM).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Dmfcc Mfcc Gmm Feature Extraction Speaker Identification

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