Objective Evaluation of the Pathological Voice Based on Deep Learning Neural Networks in an Algerian hospital environment

Abstract

In this study, we propose a method based on Recurrent Neural Networks, to objectively evaluate the process of rehabilitation of the pathological voice, in an Algerian clinical environment. We choose Unilateral Laryngeal Paralysis as the pathology of the voice. In this paper, we used a Deep Learning system of pathological voice detection by Long Short Term Memory neural model (LSTM). As the dysphony studied in our work concerns essentially the laryngeal vibration, we choose the acoustic parameters based on the instability of the frequency and the amplitude of the laryngeal vibration: Jitter and Shimmer, Noise parameters and Cepstraux MFCC coefficients (Mel Frequency Cepstral Coefficients). A pathological voice detection rate of 88.65% shows important results brought by the rehabilitation technique adopted in Algerian clinical setting. The exclusive and abusive use of hearing to evaluate the effect of speech rehabilitation in the Algerian hospital environment remains insufficient. It is important to correlate perceptual data with objective methods based on detection and classification methods by introducing relevant acoustic parameters, for an effective and objective management of vocal pathology assessment.