DocumentCode :
310465
Title :
Comparison of neural architectures for sensor fusion
Author :
Talle, Barbara ; Krone, Gabi ; Balm, G.
Author_Institution :
Dept. of Neural Inf. Processing, Ulm Univ., Germany
Volume :
4
fYear :
1997
fDate :
21-24 Apr 1997
Firstpage :
3273
Abstract :
For technical speech recognition systems as well as for humans it has been shown that the combination of acoustic and optic information can enhance speech recognition performance. But it still remains an open question, at which stage of processing the two information channels should be combined. We systematically investigate this problem by means of a neural speech recognition system applied to monosyllabic words. Different fusion architectures of multilayer perceptrons are compared both for noiseless and noisy acoustic data. Furthermore, different modularized neural architectures are examined for the acoustic channel alone. The results corroborate the idea of separate processing of the two channels until the final stage of classification
Keywords :
acoustic noise; acoustic signal processing; multilayer perceptrons; neural net architecture; optical information processing; sensor fusion; speech processing; speech recognition; acoustic channel; acoustic information; classification; fusion architectures; information channels; monosyllabic words; multilayer perceptrons; neural architectures; neural speech recognition system; noiseless acoustic data; noisy acoustic data; optic information; sensor fusion; speech recognition performance; Acoustic noise; Bandwidth; Filters; Humans; Information processing; Multilayer perceptrons; Optical sensors; Signal generators; Speech recognition; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
ISSN :
1520-6149
Print_ISBN :
0-8186-7919-0
Type :
conf
DOI :
10.1109/ICASSP.1997.595492
Filename :
595492
Link To Document :
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