Abstract
Patients suffering from dysarthria have trouble controlling their muscles involved in speaking, thereby leading to spoken speech that is indiscernible. There have been a number of studies that have addressed speech impairments; however additional research is required in terms of considering speakers with the same impairment though with variable condition of the impairment. The type of impairment and the level of severity will help in assessing the progression of the dysarthria and will also help in planning the therapy.This paper proposes the use of Convolutional Neural Network based model for identifying whether a person is suffering from dysarthria. Early diagnosis is a step towards better management of the impairment. The proposed model makes use of several speech features viz. zero crossing rates, MFCCs, spectral centroids, spectral roll off for analysis of the speech signals. TORGO speech signal database is used for the training and testing of the proposed model. CNN shows promising results for early diagnosis of dysarthric speech with an accuracy score of 93.87%.
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Dumane, P., Hungund, B., Chavan, S. (2021). Dysarthria Detection Using Convolutional Neural Network. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_45
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DOI: https://doi.org/10.1007/978-3-030-69921-5_45
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