Thomson Multitaper MFCC and PLP voice features for early detection of Parkinson disease

In this paper, MFCC and PLP voice features extracted using Single Taper Smooth (STS) window and Thomson Multitaper (TMT) windowing technique together with a neural network classifier is used in the classification of Healthy people from early stage Parkinson diseased patients and a performance comparison of the two techniques is reported. Parkinson disease in their early stages, not only affects the muscular movements of the human body but also influences the articulatory process of the speech production mechanism. This signifies change in the shape of the vocal tract which manifests itself in the short time power spectrum. The MFCC and PLP features used in this investigation, which represent the vocal tract parameters are derived from the short time spectrum. It is therefore crucial to estimate this short time power spectrum accurately. Generally, the short time speech power spectrum is estimated using STS window. But this power spectrum computed manifests large variance in the spectral estimates. Hence a variance reduced power spectrum is attained by computing the weighted average of the short time speech spectra obtained using a set of TMT windows. This spectrum is then used to compute the PLP and MFCC features. In this paper, extraction of both these voice features using STS window as well as TMT technique with three different weights namely Uniform, Eigen value (EV) and Adaptive weights is implemented using the speech samples of healthy and Parkinson diseased individuals. The experiment was carried out for several Thomson tapers ranging from 1 to 12 and the optimal number of tapers needed for the application and dataset is reported. A comparative performance analysis of the techniques implemented using both MFCC and PLP as features is then carried out in terms of classification accuracy, Equal Error Rate, sensitivity, selectivity and F1 score for the optimal taper value. The results obtained show that in comparison with the STS window a maximum improvement in the classification accuracy was obtained to be 6.6% for nine tapers, adaptive weights using MFCC as features and 6.9% for five tapers, EV weights using PLP as features for experimental dataset 1 and 6.0% using MFCC and 6.4% using PLP for experimental dataset 2. A performance improvement in other measures for the optimal taper value is also observed and reported for experimental dataset 1.