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Improving Speaker Identification Performance by Combining Vocal Tract Features

S. Selva Nidhyananthan, R. Shantha Selva Kumari, G. Jaffino Published in Pattern Recognition

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
http:/ijais12-450433
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  1. Selva S Nidhyananthan, Shantha Selva R Kumari and G Jaffino. Article: Improving Speaker Identification Performance by Combining Vocal Tract Features. International Journal of Applied Information Systems 3(1):27-33, July 2012. BibTeX

    @article{key:article,
    	author = "S. Selva Nidhyananthan and R. Shantha Selva Kumari and G. Jaffino",
    	title = "Article: Improving Speaker Identification Performance by Combining Vocal Tract Features",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 1,
    	pages = "27-33",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, 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).

Reference

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Keywords

Dmfcc, Mfcc, Gmm, Feature Extraction, Speaker Identification