Dimension reduction approaches for SVM based speaker age estimation

Gil Dobry, Ron M. Hecht, Mireille Avigal, Yaniv Zigel

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

Abstract

This paper presents two novel dimension reduction approaches applied on the gaussian mixture model (GMM) supervectors to improve age estimation speed and accuracy. The GMM supervector embodies many speech characteristics irrelevant to age estimation and like noise, they are harmful to the system's generalization ability. In addition, the support vectors machine (SVM) testing computation grows with the vector's dimension, especially when using complex kernels. The first approach presented is the weighted-pairwise principal components analysis (WPPCA) that reduces the vector dimension by minimizing the redundant variability. The second approach is based on anchor-models, using a novel anchors selection method. Experiments showed that dimension reduction makes the testing process 5 times faster and using the WPPCA approach, it is also 5% more accurate.

Original languageEnglish
Pages (from-to)2031-2034
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 26 Nov 2009
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 6 Sep 200910 Sep 2009

Keywords

  • Age estimation
  • Anchor models
  • Dimension reduction
  • GMM supervector
  • NAP
  • SVM

ASJC Scopus subject areas

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

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