Abstract
Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classi?cation problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the a Gender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.
Original language | English |
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Title of host publication | INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association |
Editors | Takao Kobayashi, Keikichi Hirose, Satoshi Nakamura |
Place of Publication | Lisbon, Portugal |
Publisher | International Speech Communication Association |
Pages | 2806-2809 |
Number of pages | 4 |
ISBN (Print) | 9781617821233 |
Publication status | Published - 2010 |
Event | INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association - Makuhari, Makuhari, Japan Duration: 26 Sept 2010 → 30 Sept 2010 |
Conference
Conference | INTERSPEECH 2010: 11th Annual Conference of the International Speech Communication Association |
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Country/Territory | Japan |
City | Makuhari |
Period | 26/09/10 → 30/09/10 |