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 language | English |
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Pages (from-to) | 2031-2034 |
Number of pages | 4 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
State | Published - 26 Nov 2009 |
Event | 10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom Duration: 6 Sep 2009 → 10 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