The aim of this study is to perform an Arabic word recognition system, focused to a small vocabulary. Various models using neural network approach have been used in ASR. In order to increase the efficiency of the classification task we propose the use of a nonparametric density estimator. Thus, in this paper we present an adaptation scheme for independent speaker Arabic speech recognition based on the General Regression Neural Network (GRNN). In another hand we have also implemented a left-right Hidden Markov Model (DHMM) with five states and relative performances of the two proposed applications are compared to the popular known MLP. Experimental results obtained with large corpora have shown that the use of a nonparametric density estimator with an appropriate smooth factor improves the generalization power of neural network.
Cite as: Amrouche, A., Rouvaen, J.M. (2004) On the use of the nonparametric regression in neural network based approach applied to Arabic speech. Proc. 9th Conference on Speech and Computer (SPECOM 2004), 276-281
@inproceedings{amrouche04_specom,
author={Abderrahmane Amrouche and Jean Michel Rouvaen},
title={{On the use of the nonparametric regression in neural network based approach applied to Arabic speech}},
year=2004,
booktitle={Proc. 9th Conference on Speech and Computer (SPECOM 2004)},
pages={276--281}
}