13 November 2003 Sparse representation in speech signal processing
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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.
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Te-Won Lee, Te-Won Lee, Gil-Jin Jang, Gil-Jin Jang, Oh-Wook Kwon, Oh-Wook Kwon, } "Sparse representation in speech signal processing", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.506153; https://doi.org/10.1117/12.506153

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