DocumentCode :
1924254
Title :
Discriminative analysis of distortion sequences in speech recognition
Author :
Chang, Pao-Chung ; Chen, Sin-Horng ; Juang, Biing-hwang
Author_Institution :
Telecommun. Lab., Minist. of Commun., Taiwan
fYear :
1991
fDate :
14-17 Apr 1991
Firstpage :
549
Abstract :
The authors suggest a linear discriminant function to complete the distance score instead of a conventional average distance. Several discriminative algorithms are proposed to learn the discriminant function. These include one heuristic method, two methods based on the error propagation algorithm, and one method based on the generalized probabilistic descent (GPD) algorithm. The authors study these methods in a speaker-independent speech recognition task involving utterances of the highly confusable English E-set. The results show that the best performance is obtained by using the GPD method, which achieved a 78.1% accuracy, compared to 67.6% with the traditional average method
Keywords :
probability; speech recognition; English E-set; discriminative algorithms; distance score; error propagation algorithm; generalized probabilistic descent; heuristic method; linear discriminant function; recognition accuracy; speaker-independent speech recognition; Distortion measurement; Dynamic programming; Heuristic algorithms; Hidden Markov models; Pattern recognition; Performance analysis; Speech analysis; Speech recognition; Timing; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
ISSN :
1520-6149
Print_ISBN :
0-7803-0003-3
Type :
conf
DOI :
10.1109/ICASSP.1991.150398
Filename :
150398
Link To Document :
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