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