10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Dimension Reduction Approaches for SVM Based Speaker Age Estimation

Gil Dobry (1), Ron M. Hecht (2), Mireille Avigal (1), Yaniv Zigel (3)

(1) Open University of Israel, Israel
(2) PuddingMedia, Israel
(3) Ben-Gurion University of the Negev, Israel

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 systemís generalization ability. In addition, the support vectors machine (SVM) testing computation grows with the vectorís 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.

Full Paper

Bibliographic reference.  Dobry, Gil / Hecht, Ron M. / Avigal, Mireille / Zigel, Yaniv (2009): "Dimension reduction approaches for SVM based speaker age estimation", In INTERSPEECH-2009, 2031-2034.