Assessment of Amplified Parkinsonian Speech Quality Using Deep Learning

In this paper, deep neural networks (DNNs) are applied to features extracted from Parkinsonian speech recordings to predict their perceived quality. This procedure was also used to benchmark the electroacoustic characteristics of speech amplifiers used by people afflicted with Parkinson Disease (PD). Speech recordings were obtained from 11 PD subjects and 10 normal controls, with and without the assistance of 7 different speech amplifiers, and their quality was assessed subjectively by normal hearing listeners. Mel-frequency and Gammatone frequency cepstral coefficients (MFCCs and GFCCs respectively) and their first order derivatives were extracted as features, and given as input to the DNN. Two optimizers were used to train the neural network, namely stochastic gradient descent (SGD) and Adam optimizers. The paper also shows the effect of feature reduction in enhancing the performance of the objective predictors. Experimental results showed that a trained DNN with reduced set of GFCC features outperforms other objective metrics in terms of correlation with the subjective measures.