Abstract
The aim of this work is to improve the automatic recognition of the dysarthria speech. In this context, we have compared two techniques of speech parameterization; these two techniques are based on the recently proposed coefficients Power Normalized Cepstral Coefficients and Mel-Frequency Cepstral Coefficients. In this paper we have concatenate several variants of JITTER and SHIMMER with the techniques of speech parameterization to improve an automatic recognition of the dysarthric word system. The aim is to help the fragile persons having speech problems (dysarthric voice) and the doctor to make a first diagnosis about the patient’s disease. For this, an Automatic Acknowledgment of Continuous Pathological Speech System has been developed based on the Hidden Models of Markov and the Hidden Markov Model Toolkit. For our tests, we used the Nemours Database which contains 11 speakers representing dysarthric voices.
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Zaidi, BF., Boudraa, M., Selouani, SA., Addou, D., Yakoub, M.S. (2020). Automatic Recognition System for Dysarthric Speech Based on MFCC’s, PNCC’s, JITTER and SHIMMER Coefficients. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_40
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DOI: https://doi.org/10.1007/978-3-030-17798-0_40
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