Optimization of discriminative kernels in SVM speaker verification

Shi Xiong Zhang, Man Wai Mak

Research output: Journal article publicationConference articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

An important aspect of SVM-based speaker verification systems is the design of sequence kernels. These kernels should be able to map variable-length observation sequences to fixed-size supervectors that capture the dynamic characteristics of speech utterances and allow speakers to be easily distinguished. Most existing kernels in SVM speaker verification are obtained by assuming a specific form for the similarity function of supervectors. This paper relaxes this assumption to derive a new general kernel. The kernel function is general in that it is a linear combination of any kernels belonging to the reproducing kernel Hilbert space. The combination weights are obtained by optimizing the ability of a discriminant function to separate a target speaker from impostors using either regression analysis or SVM training. The idea was applied to both low- and high-level speaker verification. In both cases, results show that the proposed kernels outperform the state-of-the-art sequence kernels. Further performance enhancement was also observed when the high-level scores were combined with acoustic scores.
Original languageEnglish
Pages (from-to)1275-1278
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 26 Nov 2009
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009

Keywords

  • High-level features
  • Optimal kernels
  • Sequence kernels
  • Speaker verification
  • SVM

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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