DocumentCode :
2907444
Title :
Vector-quantization-based speech recognition and speaker recognition techniques
Author :
Furui, Sadaoki
Author_Institution :
NTT Human Interface Lab., Tokyo, Japan
fYear :
1991
fDate :
4-6 Nov 1991
Firstpage :
954
Abstract :
The author reviews major methods of applying the vector quantization (VQ) technique to speech and speaker recognition. These include speech recognition based on the combination of VQ and the DTW/HMM (dynamic time warping/hidden Markov model) technique. VQ-distortion-based recognition, learning VQ algorithms, speaker adaptation by VQ-codebook mapping, and VQ-distortion-based speaker recognition. It is concluded that not only has the VQ technique reduced the amount of computation and storage, but it has also created new ideas for solving various problems in speech/speaker recognition
Keywords :
Markov processes; data compression; encoding; learning systems; speech recognition; VQ-codebook mapping; VQ-distortion-based recognition; dynamic time warping/hidden Markov model; learning VQ algorithms; speaker adaptation; speaker recognition; speech recognition; vector quantization; Hidden Markov models; Humans; Image coding; Laboratories; Nonlinear distortion; Speaker recognition; Speech coding; Speech recognition; Vector quantization; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
ISSN :
1058-6393
Print_ISBN :
0-8186-2470-1
Type :
conf
DOI :
10.1109/ACSSC.1991.186588
Filename :
186588
Link To Document :
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