Jitter as a quantitative indicator of dysphonia in Parkinson's disease
by Jennifer C. Saldanha; Malini Suvarna; Dayakshini Satish; Cynthia Santhmayor
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 21, No. 2, 2023

Abstract: A non-invasive way of diagnosing Parkinson's disease from speech signals is presented in this paper. A variety of frequency, amplitude, harmonicitynoise, and cepstral features are extracted from speech samples, resulting in a feature vector of 82 coefficients. k-nearest neighbours (k-NN) with k = 10 and artificial neural network (ANN) are applied to the dataset on individual and combined features to detect Parkinson's disease. The jitter feature obtained a maximum accuracy with both k-NN and ANN classifiers. k-NN outperformed ANN by obtaining a classification accuracy of 90% for jitter local features and 88.3% for combined features. The severity of the disease is assessed using multi-class classification, obtaining an overall accuracy of 83.6% and 82.4% for k-NN and ANN, respectively. The accuracy in detection is also verified on the dataset divided based on age and gender category. The results of the perceptual test proved that the predominant voice quality in Parkinson's disease is hoarse.

Online publication date: Mon, 19-Jun-2023

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