HMM-GMM based Amazigh speech recognition system
by Safâa El Ouahabi; Mohamed Atounti; Mohamed Bellouki
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 12, No. 1/2, 2020

Abstract: This study presents conception and realisation of an automatic independent speech recognition system using hidden Markov model (HMM). The system recognises 33 letters in Amazigh language. System is found well performed and can identify the Amazigh spoken letters at 88, 44% recognition rate, which is well acceptable rate of accuracy for speech recognition. The tests were taken based on the HMM and Gaussian mixture distributions. Hidden Markov toolkit (HTK) has been used in implementation and test phases. The word error rate (WER) came initially to 29.41 and reduced to about 11.52% thanks to extensive testing and change of the recognition's parameters.

Online publication date: Thu, 11-Mar-2021

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