The recognition of speech defects using convolutional neural network

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%.

ACNS Conference on Cloud and Immersive Technologies in Education: Report

“…The authors' related works are referenced as [84][85][86]. In their article titled "The recognition of speech defects using convolutional neural network" [124], Olha Pronina and Olena Piatykop (figure 11) present a solution to improve the recognition efficiency of speech defects in children by utilizing convolutional neural network models to process spectrogram-based sound data. The ability to communicate effectively through speech is crucial for successful integration into society, and speech development is an essential part of a child's normal growth.…”

Classification of Dyslexia Among School Students Using Deep Learning

“…One such technological approach is the utilization of spectrograms and convolutional neural networks (CNNs) for diagnosing dyslexic children. Spectrograms are visual representations of sound that provide detailed information about the frequency and intensity of different audio signal components [4]. CNNs, on the other hand, are deep learning models that excel at extracting meaningful patterns and features from complex data.…”