DocumentCode :
701492
Title :
Robust speech recognition using fuzzy matrix quantisation, neural networks and Hidden Markov models
Author :
Xydeas, C S ; Cong, Lin
Author_Institution :
Speech Processing Research Laboratory, Electrical Engineering Division, School of Engineering, University of Manchester, Dover Street, Manchester, M13 9PL, UK
fYear :
1996
fDate :
10-13 Sept. 1996
Firstpage :
1
Lastpage :
4
Abstract :
In this paper a new approach to robust speech recognition using Fuzzy Matrix Quantisation, Hidden Markov Models and Neural Networks is presented and tested when speech is corrupted by car noise. Thus two new robust isolated word speech recognition (IWSR) systems called FMQ/HMM and FMQ/MLP, are proposed and designed optimally for operation in a variety of input SNR conditions. The schemes and associated system training methodologies result into a particularly high recognition performance at input SNR levels as low as 5 and 0 dBs.
Keywords :
Hidden Markov models; Robustness; Signal to noise ratio; Speech; Speech recognition; Training; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
European Signal Processing Conference, 1996. EUSIPCO 1996. 8th
Conference_Location :
Trieste, Italy
Print_ISBN :
978-888-6179-83-6
Type :
conf
Filename :
7083218
Link To Document :
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