Comparative study of several novel acoustic features for speaker recognition

Vladimir Pervouchine, Graham Leedham, Haishan Zhong, David Cho, Haizhou Li

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Finding good features that represent speaker identity is an important problem in speaker recognition area. Recently a number of novel acoustic features have been proposed for speaker recognition. The researchers use different data sets and sometimes different classifiers to evaluate the features and compare them to the baselines such as MFCC or LPCC. However, due to different experimental conditions direct comparison of those features to each other is difficult or impossible. This paper presents a study of five new recently proposed acoustic features using the same data (NIST 2001 SRE), and the same UBM-GMM classifier. The results are presented as DET curves with equal error ratios indicated. Also, an SVM-based combination of GMM scores produced on different features has been made to determine if the new features carry any complimentary information. The results for different features as well as for their combinations are directly comparable to each other and to those obtained with the baseline MFCC features.

Original languageEnglish
Title of host publicationBIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing
Pages220-223
Number of pages4
Publication statusPublished - 2008
Externally publishedYes
EventBIOSIGNALS 2008 - 1st International Conference on Bio-inspired Systems and Signal Processing - Funchal, Madeira, Portugal
Duration: 28 Jan 200831 Jan 2008

Publication series

NameBIOSIGNALS 2008 - Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing
Volume1

Conference

ConferenceBIOSIGNALS 2008 - 1st International Conference on Bio-inspired Systems and Signal Processing
Country/TerritoryPortugal
CityFunchal, Madeira
Period28/01/0831/01/08

Keywords

  • Feature evaluation
  • Feature extraction
  • Speaker recognition

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Control and Systems Engineering

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