Title :
Hierarchical Structures of Neural Networks for Phoneme Recognition
Author :
P. Schwarz;P. Matejka;J. Cernocky
Author_Institution :
Speech@FIT group, Brno University of Technology, Czech Republic, schwarzp@fit.vutbr.cz
fDate :
6/28/1905 12:00:00 AM
Abstract :
This paper deals with phoneme recognition based on neural networks (NN). First, several approaches to improve the phoneme error rate are suggested and discussed. In the experimental part, we concentrate on temporal patterns (TRAPs) and novel split temporal context (STC) phoneme recognizers. We also investigate into tandem NN architectures. The results of the final system reported on standard TIMIT database compare favorably to the best published results
Keywords :
"Neural networks","Hidden Markov models","Error analysis","Multilayer perceptrons","Speech recognition","Keyword search","Artificial neural networks","Recurrent neural networks","Training data","Dynamic range"
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Print_ISBN :
1-4244-0469-X
Electronic_ISBN :
2379-190X
DOI :
10.1109/ICASSP.2006.1660023
