Assessment of Speech Quality during Speech Rehabilitation Based on the Solution of the Classification Problem

Abstract

The article considers an approach to the problem of assessing the quality of speech during speech rehabilitation as a classification problem. For this, a classifier is built on the basis of an LSTM neural network for dividing speech signals into two classes: before the operation and immediately after. At the same time, speech before the operation is the standard to which it is necessary to approach in the process of rehabilitation. The metric of belonging of the evaluated signal to the reference class acts as an assessment of speech. An experimental assessment of rehabilitation sessions and a comparison of the resulting assessments with expert assessments of phrasal intelligibility were carried out.

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

“…Furthermore, the neural networks had a layer size of 10, 25 and 100 for the narrow, medium, and wide models, respectively, while the multilayered models had a uniform layer size of 10 each. The selection of the referenced models was made following a comprehensive review of the literature on the topic, with the goal of identifying the most effective and commonly employed ML model in the field of speech classification [51,52]. Further details on the structure of the database are reported in Table 1.…”