DocumentCode :
1661722
Title :
Squad-based expert modules for closing diphthong recognition
Author :
Kirkland, John
Author_Institution :
Dept. of Electr. & Electron. Eng., Canterbury Univ., Christchurch, New Zealand
fYear :
1995
Firstpage :
302
Lastpage :
305
Abstract :
The paper presents a new method of forming expert modules for modular time delay neural networks (modular TDNNs) using ensembles of similarly trained TDNNs referred to as squads. Squad base expert modules for closing diphthong recognition are compared with traditional expert modules comprising individual TDNNs and are found to afford significantly better false positive error performances, while recognition performances are comparable or better. Traditional and squad based expert modules formed from three different TDNN variants are compared. One of these variants, sequence token TDNN, embodies a novel method of using traditional TDNNs for closing diphthong recognition and is found to outperform the other variants when squad based expert modules are used
Keywords :
expert systems; natural languages; neural nets; speech recognition; TDNN variants; automated speech recognition; closing diphthong recognition; false positive error performances; modular TDNNs; modular time delay neural networks; phoneme realizations; recognition performance; sequence token TDNN; squad based expert modules; Automatic speech recognition; Frequency domain analysis; Natural languages; Neural networks; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-7174-2
Type :
conf
DOI :
10.1109/ANNES.1995.499494
Filename :
499494
Link To Document :
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