Deep Neural Network for Automatic Assessment of Dysphonia

2022/02/25 by García, Mario Alejandro, Rosset, Ana Lorena
#Audio and Speech Processing (eess.AS) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Quantitative Methods (q-bio.QM) #Sound (cs.SD) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2202.12957

Abstract

The purpose of this work is to contribute to the understanding and improvement of deep neural networks in the field of vocal quality. A neural network that predicts the perceptual assessment of overall severity of dysphonia in GRBAS scale is obtained. The design focuses on amplitude perturbations, frequency perturbations, and noise. Results are compared with performance of human raters on the same data. Both the precision and the mean absolute error of the neural network are close to human intra-rater performance, exceeding inter-rater performance.

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