Novel Linear Prediction Temporal Phase Based Features for Speaker Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10458)

Abstract

This paper proposes novel features based on linear prediction of temporal phase (LPTP) for speaker recognition task. The proposed LPTC feature vector represents Discrete Cosine Transform (DCT) (for energy compaction and decorrelation) coefficients of LP spectrum derived from temporal phase of speech signal. The results are shown on standard NIST 2002 SRE and GMM-UBM (Gaussian Mixture Modeling-Universal Background Modeling) approach. A recently proposed supervised score-level fusion method is used for combining evidences of Mel Frequency Cepstral Coefficients (MFCC) and proposed feature set. Performance of proposed feature set is compared with state-of-the-art MFCC features. It is evident from the results that proposed features gives 4% improvement in % identification rate and 2% decrement in % EER than that of standard MFCC alone. In addition, when the supervised score-level fusion is used, identification rate improves 8% and EER is decreased by 2% indicating that proposed feature captures complimentary information than MFCC alone.

Keywords

Linear prediction (LP) spectrum Temporal phase Speaker recognition Score-level fusion 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Infinium Solutionz Pvt LtdAhmedabadIndia
  2. 2.Dhirubhai Ambani Institute of Information Communication and TechnologyGandhinagarIndia

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