Abstract
Two class-separability criteria based on the divergence measure are proposed to improve speech recognition performance. The average and weighted average divergence measures are used as criteria for finding a transformation matrix which maps the original features into a more discriminative subspace. Results are presented for a highly confusable task.
Original language | English (US) |
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Pages (from-to) | 343-345 |
Number of pages | 3 |
Journal | IEEE Transactions on Speech and Audio Processing |
Volume | 7 |
Issue number | 3 |
DOIs | |
State | Published - 1999 |
Externally published | Yes |
ASJC Scopus subject areas
- Software
- Acoustics and Ultrasonics
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering