Product of power spectrum and group delay function for speech recognition

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Title Product of power spectrum and group delay function for speech recognition
Author Zhu, Donglai; Paliwal, Kuldip Kumar
Publication Title Acoustics, Speech, and Signal Processing (ICASSP), 2004 IEEE International Conference
Editor Douglas O'Shaughnessy (General Chair)
Year Published 2004
Place of publication Piscataway, N.J.
Publisher IEEE
Abstract Mel-frequency cepstral coefficients (MFCCs) are the most widely used features for speech recognition. These are derived from the power spectrum of the speech signal. Recently, the cepstral features derived from the modified group delay function (MGDF) have been studied by Murthy and Gadde [6] for speech recognition. In this paper, we propose to use the product of the power spectrum and the group delay function (GDF), and derive the MFCCs from the product spectrum. This spectrum combines the information from the magnitude spectrum as well as the phase spectrum. The MFCCs of the MGDF are also investigated in this paper. Results show that the cepstral features derived from the power spectrum perform better than that from the MGDF, and the product spectrum based features provide the best performance.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/servlet/opac?punumber=9248
Alternative URI http://dx.doi.org/10.1109/ICASSP.2004.1325938
Copyright Statement Copyright 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 0-7803-8484-9
Conference name 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
Location Montreal, Canada
Date From 2004-05-17
Date To 2004-05-21
URI http://hdl.handle.net/10072/2111
Date Accessioned 2005-03-31
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Speech Recognition
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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