Dysphonia Measurements Detection Using CQT’s and MFCC’s Methods

© 2020, Springer Nature Switzerland AG. Dysphonia is a vocal impediment that appears as a symptom of Parkinson’s disease, and can be used for its diagnosis. Among the important measurements for dysphonia detection are jitter, shimmer, fundamental frequency (F0), Harmonics to noise ratio (HNR) and noise to harmonics ratio (NHR). The frequency space of the speech signal is used to detect these five dysphonia measurements, through this space the acoustic markers jitter, shimmer and F0 are calculated. In this article, an evaluation of the detection of acoustic markers is presented through the mathematical methods of the Constant Q Transform (CQT) and the Mel Frequencies Cepstral Coefficients (MFCC) in speech signals of patients with Parkinson’s disease. The classifier method Support Vector Machine (SVM) is used to detect the Biomarkers. According to the results, the CQT method and MFCC method (57% and 62% precision respectively) which is a promising results for Parkinson’s disease diagnosis by the detection of Dysphonia measurements.