Optimization of Entropy-Based Automated Dyslalia Screening Algorithm
Emilian Erman Mahmut, D. Berian, Della Ventura Michele, Stoicu Tivadar Vasile · Studies in health technology and informatics · 2020
This paper presents the current state of progress of a project aimed at achieving an automated information entropy-based discrimination of phoneme mispronunciations in utterances of early school-age children. The introductory part briefly describes the dyslalia symptomology and the incidence of dyslalic disorders. This section also reviews the current challenges posed by the main research objective in other similar projects sharing the same objective and summarizes the current results thereof. The Material and Method section presents the conditions, the technology and the feature-extraction technique used in the experiment. The same section also describes the computation of the information entropy values of each analyzed speech sample. The highest match rate of 93.33% was achieved in the classification of words containing the phoneme /r/ in the initial position. A synthesis of the achieved results is provided in the Results section based on which conclusions are drawn and exposed in the Discussion and Conclusions section.