DocumentCode :
1775364
Title :
Matrix-MCE based fuzzy neural network for speech recognition
Author :
Gin-Der Wu ; Zhen-Wei Zhu
Author_Institution :
Dept. of Electr. Eng., Nat. Chi Nan Univ., Puli, Taiwan
fYear :
2014
fDate :
18-20 June 2014
Firstpage :
546
Lastpage :
550
Abstract :
Matrix-MCE (MMCE) based fuzzy neural network (FNN) for speech recognition is proposed in this paper. The environment noises usually degrade the performance of speech recognition. To reduce the effect of noises, MMCE is applied to minimize the classification error of two-dimension-cepstrum (TDC). Then the template matching employs FNN. To evaluate the performance, the speech data used for our experiments are a set of isolated Mandarin digits. Experimental results indicate that MMCE-based FNN works better than the other methods.
Keywords :
fuzzy neural nets; matrix algebra; pattern classification; pattern matching; speech recognition; FNN; MMCE-based FNN; TDC; environment noises; isolated Mandarin digits; matrix-MCE based fuzzy neural network; minimum classification error; speech data; speech recognition performance; template matching; two-dimensioncepstrum; Fuzzy neural networks; Noise; Noise measurement; Principal component analysis; Robustness; Speech; Speech recognition; fuzzy neural network; speech recognition; two-dimension-cepstrum;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control & Automation (ICCA), 11th IEEE International Conference on
Conference_Location :
Taichung
Type :
conf
DOI :
10.1109/ICCA.2014.6870978
Filename :
6870978
Link To Document :
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