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HMM-GMM based Amazigh speech recognition system

HMM-GMM based Amazigh speech recognition system 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2020.113564
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2020

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