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Proceedings Paper

Sparse representation in speech signal processing
Author(s): Te-Won Lee; Gil-Jin Jang; Oh-Wook Kwon
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Paper Abstract

We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals. This representation leads to extremely sparse priors that can be used for encoding speech signals for a variety of purposes. We review applications of this method for speech feature extraction, automatic speech recognition and speaker identification. Furthermore, this method is also suited for tackling the difficult problem of separating two sounds given only a single microphone.

Paper Details

Date Published: 13 November 2003
PDF: 10 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.506153
Show Author Affiliations
Te-Won Lee, Univ. of California/San Diego (United States)
Gil-Jin Jang, Korea Advanced Institute of Science and Technology (South Korea)
Oh-Wook Kwon, Univ. of California/San Diego (United States)

Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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