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