Optimal parameters selected for automatic recognition of spoken Amazigh digits and letters using Hidden Markov Model Toolkit

Abstract

In this paper, we present our Amazigh automatic speech recognition system. Its realization is constructed with context-independent phonetic Hidden Markov Models. Many choices are made on this system, such as the number of states of the models, the type of emission probability densities associated with the states, and the representation of the signal by cepstral coefficients. The results of recognition of our system place it at a level of height performance comparable to that achieved by Markovian automatic speech recognition systems. Our system is designed to recognize 43 distinct isolated Amazigh words (33 letters and 10 digits). The recognition rate is then calculated for each digit and letter. The overall accuracy and word recognition rate for the whole database achieved 91.31% after extensive testing and change of the recognition parameters. The results obtained in this work are improved in association with our previous work concerning Amazigh spoken digits and letters automatic speech recognition, using Hidden Markov Model Toolkit.