DocumentCode :
2018681
Title :
Coordinated training of noise removing networks
Author :
Moon, Seokyong ; Hwang, Jenq-Neng
Author_Institution :
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
Volume :
1
fYear :
1993
fDate :
27-30 April 1993
Firstpage :
573
Abstract :
When a recognition system designed with a noise-free speech signal is deployed in the real world, it experiences severe degradation due to environmental acoustic ambient noise. An enhancement technique based on a noise removing network (NRN) which has recurrent connections is proposed. This NRN is trained in coordination with a time delay neural network (TDNN) which has been trained for noise-free speech recognition tasks. The proposed enhancement technique has favorable performance for white/colored Gaussian noisy speech when compared with the NRN without coordinated training and the classical linear Wiener filtering methods.<>
Keywords :
environmental degradation; learning (artificial intelligence); recurrent neural nets; speech recognition; white noise; co-ordinated training; degradation; environmental acoustic ambient noise; noise removing networks; noise-free speech recognition; performance; time delay neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location :
Minneapolis, MN, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.1993.319183
Filename :
319183
Link To Document :
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