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.
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.