Using neural networks to correct speech defects in children.
Abstract
Determination of speech defects in a child is an urgent scientific and practical task, since timely correction of speech can improve the child's communication skills in the future. Based on the analysis given in the work, it is logical to use convolutional neural networks as a tool to identify a defect and its type. The work is devoted to the development of an algorithm for the identified speech defect, which includes the preparation of data for training the model and the use of a convolutional neural network that was developed. The architecture of a convolutional neural network is described. Experiments were carried out to verify the accuracy and adequacy of the developed neural network model, the results are presented in the form of tabular data.
References
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