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

Publication date: September 2018Source: Biomedical Signal Processing and Control, Volume 46Author(s): Savitha S. Upadhya, A.N. Cheeran, J.H. NirmalAbstractIn 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 dif...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research