Fast word detection in a speech using new high speed time delay neural networks

Authors:
Hazem M. El-Bakry;Nikos Mastorakis
Affiliations:
Faculty of Computer Science & Information Systems, Mansoura University, Egypt;Technical University of Sofia, Bulgaria
Venue:
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Year:
2009

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Abstract

This paper presents a new approach to speed up the operation of time delay neural networks for fast detecting a word in a speech. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.