Amazigh Isolated-Word speech recognition system using Hidden Markov Model toolkit (HTK)

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Abstract

This paper aims to build a speaker-independent automatic Amazigh Isolated-Word speech recognition system. Hidden Markov Model toolkit (HTK) that uses hidden Markov Models has been used to develop the system. The recognition vocabulary consists on the Amazigh Letters and Digits. The system has been trained to recognize the Amazigh 10 first digits and 33 alphabets. Mel frequency spectral coefficients (MFCCs) have been used to extract the feature. The training data has been collected from 60 speakers including both males and females. The test-data used for evaluating the system-performance has been collected from 20 speakers. The experimental results show that the presented system provides the overall word-accuracy 80%. The initial results obtained are very satisfactory in comparison with the training database's size, this encourages us to increase system performance to achieve a higher recognition rate.

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