The recognition of speech defects using convolutional neural network

Authors

DOI:

https://doi.org/10.55056/cte.554

Keywords:

speech defects, smart data processing, CNN, model of a convolutional neural network, Deep Learning

Abstract

The paper proposes a solution to improve the efficiency of recognition of speech defects in children by processing the sound data of the spectrogram based on convolutional neural network models. For a successful existence in society, a person needs the most important skill - the ability to communicate with other people. The main part of the information a person transmits through speech. The normal development of children necessarily includes the mastery of coherent speech. Speech is not an innate skill for people, and children learn it on their own. Speech defects can cause the development of complexes in a child. Therefore, it is very important to eliminate them at an early age. So, the problem of determining speech defects in children today is a very urgent problem for parents, speech therapists and psychologists. Modern information technologies can help in solving this problem. The paper provides an analysis of the literature, which showed that models of CNN can be successfully used for this. But the results that are available today have not been applied to speech in Ukrainian. Therefore, it is important to develop and study models and methods of convolutional neural networks to identify violations in the speech of children. The paper describes a mathematical model of oral speech disorders in children, the structure of a convolutional neural network and the results of experiments. The results obtained in the work allow to establish one of the speech defects: dyslexia, stuttering, difsonia or dyslalia with recognition results of 77-79%.

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Published

2023-03-21

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Section

Cloud-based AI Education Applications

How to Cite

Pronina, O. and Piatykop, O., 2023. The recognition of speech defects using convolutional neural network. CTE Workshop Proceedings [Online], 10, pp.153–166. Available from: https://doi.org/10.55056/cte.554 [Accessed 22 July 2024].
Received 2022-10-22
Accepted 2022-12-22
Published 2023-03-21

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