Squad-based expert modules for closing diphthong recognition
- 19 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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
This publication has 6 references indexed in Scilit:
- Integrated training for spotting Japanese phonemes using large phonemic time-delay neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- TDNN-LR continuous speech recognition system using adaptive incremental TDNN trainingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Speaker-independent phoneme recognition on TIMIT database using integrated time-delay neural networks (TDNNs)Published by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Phoneme recognition using time-delay neural networksIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Modularity and scaling in large phonemic neural networksIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Comparative Study of Nonlinear Time Warping Techniques in Isolated Word Speech Recognition SystemsPublished by Defense Technical Information Center (DTIC) ,1981