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ICASSP
2010
IEEE

Evaluation of random-projection-based feature combination on speech recognition

14 years 5 days ago
Evaluation of random-projection-based feature combination on speech recognition
Random projection has been suggested as a means of dimensionality reduction, where the original data are projected onto a subspace using a random matrix. It represents a computationally simple method that approximately preserves the Euclidean distance of any two points through the projection. Moreover, as we are able to produce various random matrices, there may be some possibility of finding a random matrix that gives a better speech recognition accuracy among these random matrices. In this paper, we investigate the feasibility of random projection for speech feature extraction. To obtain an optimal result from among many (infinite) random matrices, a vote-based random-projection combination is introduced in this paper, where ROVER combination is applied to random-projectionbased features. Its effectiveness is confirmed by word recognition experiments.
Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yas
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Tetsuya Takiguchi, Jeff Bilmes, Mariko Yoshii, Yasuo Ariki
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