DocumentCode :
2288239
Title :
Parallel neural networks for speaker-independent all-Chinese-syllable speech recognition
Author :
Ditang, Fang
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
fYear :
1994
fDate :
13-16 Apr 1994
Firstpage :
331
Abstract :
The paper presents a parallel neural networks approach to speaker-independent all-Chinese-syllable speech recognition. The author uses neural networks to recognize 59 speech units, 22 initial consonants, 36 syllable finals and a background noise, for pattern division. Each binary classifier recognizes a speech unit to discriminate Pi from ~Pi (NoT Pi). The utterances from 137 male speakers are used to train and the utterances from other 11 male speakers are used to recognize. It achieved a recognition correct rate of 66.14% for Chinese syllables, 73.06% for initial consonants and 84.6% for syllable finals. They are rather good without grammer
Keywords :
learning (artificial intelligence); linguistics; natural languages; neural nets; parallel algorithms; speech recognition; background noise; binary classifier; grammer; initial consonants; male speakers; parallel neural networks; pattern division; recognition correct rate; speaker-independent all-Chinese-syllable speech recognition; syllable finals; training; Background noise; Computer architecture; Computer science; Laboratories; Microcomputers; Natural languages; Neural networks; Robustness; Speech enhancement; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
Type :
conf
DOI :
10.1109/SIPNN.1994.344899
Filename :
344899
Link To Document :
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