This paper investigated subjective and objective assessment of Parkinsonian speech quality. Speech stimuli were recorded from 11 Parkinsonian and 10 age-matched normal control participants under different amplification and environmental conditions. Quality ratings of the recorded stimuli were obtained from naïve listeners. For objective assessment, feature vectors were derived from the speech recordings based on temporal, spectral, and/or cepstral parametrization. These feature vectors were subsequently mapped to the predicted quality scores through several regression methods, including support vector regression, Gaussian process regression, and deep learning. Analyses of subjective speech quality ratings showed that Parkinsonian speech quality was significantly poorer than control subjects’ speech quality, and that the amplification devices differentially affected perceived quality of Parkinsonian speech. Objective analyses revealed disparity in performance among feature vectors and mappers, with some feature vector and mapper combinations exhibiting statistically similar correlations with subjective ratings. A set consisting of cepstral, spectral, and modulation domain speech features when combined with Gaussian process regression or deep learning resulted in the highest correlation of 0.85 with the subjective data.
