Variable Length Teager Energy Based Mel Cepstral Features for Identification of Twins

  • Hemant A. Patil
  • Keshab K. Parhi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

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.

Keywords

Speech Signal Vocal Tract Speaker Recognition Pitch Contour Dependency Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hemant A. Patil
    • 1
  • Keshab K. Parhi
    • 2
  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyDA-IICTGandhinagarIndia
  2. 2.Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisUSA

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