Text-dependent speaker recognition using wavelets and neural networks

Lim, Chee Peng and Woo, Siew Chan 2007, Text-dependent speaker recognition using wavelets and neural networks, Soft computing, vol. 11, no. 6, pp. 549-556.

Attached Files
Name Description MIMEType Size Downloads

Title Text-dependent speaker recognition using wavelets and neural networks
Author(s) Lim, Chee Peng
Woo, Siew Chan
Journal name Soft computing
Volume number 11
Issue number 6
Start page 549
End page 556
Total pages 8
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2007-04
ISSN 1432-7643
1433-7479
Keyword(s) adaptive resonance theory
daubechies wavelet
discrete wavelet transform
neural networks
text-dependent speaker recognition
Summary An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048793

Document type: Journal Article
Collection: Institute for Frontier Materials
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 36 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 26 Sep 2012, 09:30:48 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.