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Proceedings Paper

Nonlinear techniques for parameter extraction from quasi-continuous wavelet transform with application to speech
Author(s): Stephane Maes
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Paper Abstract

Speaker identification and word spotting will shortly play a key role in a lot of different fields. This paper presents an approach, based on the wavelet transform, to extract features from a speech signal. These features are based on the `modulation model'. An adequate choice of the extracted features dramatically increases the efficiency of the classification performed on the different speakers or on the different words.

Paper Details

Date Published: 1 February 1994
PDF: 12 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172510
Show Author Affiliations
Stephane Maes, Rutgers Univ. (United States)


Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike M.D.; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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