Efficient MLP constructive training algorithm using a neuron recruiting approach for isolated word recognition system

Authors:
Sabeur Masmoudi;Mondher Frikha;Mohamed Chtourou;Ahmed Ben Hamida
Affiliations:
Technologie de l'information et électronique médicale, TIEM --- LETI, ENIS, Sfax University, Sfax, Tunisie;Technologie de l'information et électronique médicale, TIEM --- LETI, ENIS, Sfax University, Sfax, Tunisie;Intelligent Control and Optimized System unit, ICOS, ENIS, Sfax University, Sfax, Tunisie;Technologie de l'information et électronique médicale, TIEM --- LETI, ENIS, Sfax University, Sfax, Tunisie
Venue:
International Journal of Speech Technology
Year:
2011

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Abstract

This paper describes an efficient constructive training algorithm using a Multi Layer Perceptron (MLP) neural network dedicated for Isolated Word Recognition (IWR) systems. Incremental training procedure was employed and this approach was based on novel hidden neurons recruiting for a single hidden-layer. During Neural Network (NN) training phase, the number of pronunciation samples extracted from the Training Data (TD) was sequentially increased. Optimal structure of the NN classifier with optimized TD size was obtained using this proposed MLP constructive training algorithm.Isolated word recognition system based on MLP neural network was then constructed and tested for recognizing ten words extracted from TIMIT database. Mel Frequency Cepstral Coefficient (MFCC) feature extraction method was employed including energy, first and second derivative coefficients.A proposed Frame-by-Frame Neural Network (FFNN) classification method was explored and compared with the Conventional Neural Network (CNN) classification approach. Principal Component Analysis (PCA) technique was also investigated in order to reduce both TD size as well as recognition system complexity.Experimental results showed superior performance of the proposed FFNN classifier compared to the CNN counter part which was illustrated by the significant improvement obtained in terms of recognition rate.