An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing

Abstract

The purpose of this research was to develop an artificial intelligence-based method for evaluating substitution voicing (SV) and speech following laryngeal oncosurgery. Convolutional neural networks were used to analyze spoken audio sources. A Mel-frequency spectrogram was employed as input to the deep neural network architecture. The program was trained using a collection of 309 digitized speech recordings. The acoustic substitution voicing index (ASVI) model was elaborated using regression analysis. This model was then tested with speech samples that were unknown to the algorithm, and the results were compared to the auditory-perceptual SV evaluation provided by the medical professionals. A statistically significant, strong correlation with rs = 0.863 (p = 0.001) was observed between the ASVI and the SV evaluation performed by the trained laryngologists. The one-way ANOVA showed statistically significant ASVI differences in control, cordectomy, partial laryngectomy, and total laryngectomy patient groups (p < 0.001). The elaborated lightweight ASVI algorithm reached rapid response rates of 3.56 ms. The ASVI provides a fast and efficient option for SV and speech in patients after laryngeal oncosurgery. The ASVI results are comparable to the auditory-perceptual SV evaluation performed by medical professionals.