Discriminating Parkinson diseased and healthy people using modified MFCC filter bank approach

Abstract

In this paper a modified Mel scaled filter bank-based approach to discriminate people suffering from Parkinson disease (PD) in their early stages from healthy people using speech samples is proposed. Parkinson’s disease not only affects the muscular activities of the human body but also affects the speech of the diseased. So, the speech features of Parkinson affected people tend to vary and hence differ from the speech features of healthy people. In this paper, the speech feature used for discriminating the two groups is the Mel frequency cepstral coefficients (MFCC) extracted from speech samples of both the PD and healthy people. The traditional way of computing the MFCC coefficients involves the design of the Mel filter bank. These filters are usually designed according to the auditory or acoustic system of human ear which follows the Mel scale. In this study, modification to this Mel scaled bank of filters is done by varying its bandwidth in the region of interest to compute the feature, MFCC and its performance is then compared with the conventionally designed MFCC filter bank for the said application. The performance is compared in terms of classification accuracy using radial basis network classifier. The results show an improvement of 6.3% in the classification accuracy obtained using the proposed method.