ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Dimension reduction approaches for SVM based speaker age estimation

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

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 weightedpairwise 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.

doi: 10.21437/Interspeech.2009-584

Cite as: Dobry, G., Hecht, R.M., Avigal, M., Zigel, Y. (2009) Dimension reduction approaches for SVM based speaker age estimation. Proc. Interspeech 2009, 2031-2034, doi: 10.21437/Interspeech.2009-584

  author={Gil Dobry and Ron M. Hecht and Mireille Avigal and Yaniv Zigel},
  title={{Dimension reduction approaches for SVM based speaker age estimation}},
  booktitle={Proc. Interspeech 2009},