Fast GMM computation for speaker verification using scalar quantization and discrete densities

Guoli Ye, Brian Mak, Man Wai Mak

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Most of current state-of-the-art speaker verification (SV) systems use Gaussian mixture model (GMM) to represent the universal background model (UBM) and the speaker models (SM). For an SV system that employs log-likelihood ratio between SM and UBM to make the decision, its computational efficiency is largely determined by the GMM computation. This paper attempts to speedup GMM computation by converting a continuous-density GMM to a single or a mixture of discrete densities using scalar quantization. We investigated a spectrum of such discrete models: from high-density discrete models to discrete mixture models, and their combination called high-density discrete-mixture models. For the NIST 2002 SV task, we obtained an overall speedup by a factor of 2-100 with little loss in EER performance.
Original languageEnglish
Pages (from-to)2327-2330
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

  • Discrete mixture HMM
  • High density discrete HMM
  • Scalar quantization
  • Speaker verification

ASJC Scopus subject areas

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

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