Evaluation of the syllables pronunciation quality in speech rehabilitation through the solution of the classification problem

TL;DR

This paper presents a neural network-based method to evaluate syllable pronunciation quality in speech rehabilitation by classifying speech samples before and after surgery, aiding in assessing rehabilitation progress.

Contribution

It introduces a novel classification approach using LSTM neural networks to assess pronunciation quality during speech rehabilitation, considering patient-specific factors.

Findings

LSTM classifier effectively distinguishes pre- and post-surgery speech.

Pronunciation quality assessment improves with consideration of phonemes and patient characteristics.

The method outperforms existing assessment techniques.

Abstract

The solution of the problem of assessing the quality of the pronunciation of syllables during speech rehabilitation after surgical treatment of oncological diseases of the organs of the speech-forming tract is considered in the work. The assessment is carried out by solving the problem of classifying syllables into two classes: before and immediately after surgical treatment. A classifier is built on the basis of the LSTM neural network and trained on the records before the operation and immediately after it, before the start of speech rehabilitation. The measure of assessing the quality of syllables pronunciation in the process of rehabilitation is the metric of belonging to the class before the operation. A study is being made of the influence of taking into account problematic phonemes, the gender of the patient, his individual characteristics on the resulting estimates of the quality of pronunciation. A comparison with existing types of syllable pronunciation quality assessments is carried out, recommendations are given for the practical application of the resulting new class of pronunciation quality assessments.