Share Email Print

Proceedings Paper

Nonlinear techniques for parameter extraction from quasi-continuous wavelet transform with application to speech
Author(s): Stephane Maes
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

© SPIE. Terms of Use
Back to Top