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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Interface of an Automatic Recognition System for Dysarthric Speech
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090970
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 9, 2018.
Abstract: This paper addresses the realization of a Human/Machine (H/M) interface including a system for automatic recognition of the Continuous Pathological Speech (ARSCPS) and several communication tools in order to help frail people with speech problems (Dysarthric speech) to access services providing by new technologies of information and communication (TIC) while making it easier for the doctors to achieve a first diagnosis on the patient’s disease. In addition, an ARSCPS has been improved and developed for normal and pathology voice while establishing a link with our graphic interface which is based on the box tools Hidden Markov Model Toolkit (HTK), in addition to the Hidden Models of Markov (HMM). In our work we used different techniques of feature extraction for the speech recognition system in order to improve the dysarthric speech intelligibility while developing an ARSCPS which can perform well for pathological and normal speakers. These techniques are based on the coefficients of ETSI standard Mel Frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Perceptual Linear Prediction coefficients (PLP); Mel Frequency Cepstral Coefficients (MFCC) and the recently proposed Power Normalized Cepstral Coefficients (PNCC) have been used as a basis for comparison. In this context we used the Nemours database which contains 11 speakers that represents dysarthric speech and 11 speakers that represents normal speech.
Keywords: Automatic Recognition System of Continuous Pathological Speech (ARSCPS); ETSI standard Mel frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Hidden Markov Model Toolkit (HTK); Hidden Models of Markov (HMM); Human/Machine (H/M); Technologies of
Brahim- Fares Zaidi, Malika Boudraa, Sid-Ahmed Selouani and Djamel Addou, “Interface of an Automatic Recognition System for Dysarthric Speech” International Journal of Advanced Computer Science and Applications(IJACSA), 9(9), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090970
@article{Zaidi2018,
title = {Interface of an Automatic Recognition System for Dysarthric Speech},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090970},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090970},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Brahim- Fares Zaidi and Malika Boudraa and Sid-Ahmed Selouani and Djamel Addou}
}
