DocumentCode
2493478
Title
Wavelet-FILVQ classifier for speech analysis
Author
Scheunders, Paul
Volume
4
fYear
1996
fDate
25-29 Aug 1996
Firstpage
214
Abstract
This paper describes a novel speech signal classification scheme based on spectrograms which are subjected to wavelet transform: a procedure which yields specific information regarding time and frequency variation of the signal. Feature vectors are extracted and classified using LVQ networks. The output of the network is interpreted as a fuzzy membership coefficient. This scheme is applied to the classification of voice dysphonia
Keywords
fuzzy neural nets; feature vector extraction; frequency variation; fuzzy membership coefficient; learning vector quantisation network; spectrograms; speech signal classification; time variation; voice dysphonia classification; wavelet transform; Feature extraction; Fuzzy sets; Hospitals; Neural networks; Spectrogram; Speech analysis; Speech recognition; Time frequency analysis; Wavelet analysis; Wavelet transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.547418
Filename
547418
Link To Document