Variable length teager energy based mel cepstral features for identification of twins

Hemant A. Patil, Keshab K. Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

An important issue which must be addressed for the speaker recognition system is how well the system resists the effects of determined mimics such as those based on physiological characteristics especially twins. In this paper, a new feature set based on recently proposed Variable Length Teager Energy Operator (VTEO) and state-of-the-art Mel frequency cepstral coefficients (MFCC) is developed. The effectiveness of the newly derived feature set in identifying twins in Marathi language has been demonstrated. Polynomial classifiers of 2 nd and 3 rd order have been used. The results have been compared with other spectral feature sets such as Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and baseline MFCC.

Original languageEnglish (US)
Title of host publicationPattern Recognition and Machine Intelligence - Third International Conference, PReMI 2009, Proceedings
Pages525-530
Number of pages6
DOIs
StatePublished - 2009
Event3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009 - New Delhi, India
Duration: Dec 16 2009Dec 20 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5909 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009
CountryIndia
CityNew Delhi
Period12/16/0912/20/09

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