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Acoustic analysis of voice is a non invasive, reliable, easy to use and cost effective method in detecting parkinson disease. Voice deviation from normal one is the earliest indicator of parkinson disease. Voice data of sustained phonation is collected from 25 healthy and 22 parkinson subjects. The voice database is analyzed and acoustic features are extracted. Two new parameters ECP (energy between consecutive peaks) and ASR (average slew rate) are defined. The values of these parameters show variation among two groups. A row vector is prepared using these parameters and fed to the classifiers. ANN (artificial neural network) with Cascade, Feedforwad and Elman back prop functions, SVM (support vector machine), Discriminant analysis with linear and diaglinear functions are used as classifiers and their performances are compared. SVM has been tested to be the best one and gives the accuracy of 86%. Performances of classifiers are evaluated in terms of sensitivity, specificity and accuracy.

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Human speech signal is an acoustic wave, which conveys the information about the words or message being spoken, identity of the speaker, language spoken, the presence and type of speech pathologies, the physical and emotional state of the speaker. Speech under physical task stress shows variations from the speech in neutral state and thus degrades the speech system performance. In this paper we have characterized the voice samples under physical stress and the acoustic parameters are compared with the neutral state voice parameters. The traditional voice measures, glottal flow parameters, mel frequency cepstrum coefficients and energy in various frequency bands are used for this characterization. T-test is performed to check the statistical significance of parameters. Significant variations are noticed in the parameters under two states. Pitch, intensity, energy values are high for the physically stressed voice; On the other hand glottal parameter values get decreased. Cepstrum coefficients shift up from the coefficients of neutral state voice samples. Energy in lower frequency bands was more sensitive to physical stress. This study improves the performance of various speech processing applications by analyzing the unwanted effect of physical stress in voice.