Vowel’s classification for stroke patients through rehabilitation performance via image-profiled sound data

Abstract

In terms of medicine, a disorder is a disturbance of the mind or body's normal functioning. Communication issues may result from stroke because it damages the parts of the brain that control language. The capacity to talk, read, write, and comprehend speeches can all be affected after a stroke. The rehabilitation and treatment of patients generally take a long time and include constant medication, exercise, and rehabilitation training. However, most rehab facilities throughout the world still manually carry out this rehabilitation process. Machine learning and deep learning have been introduced to this medical field to aid rehabilitation using the new technology due to computer vision's impact on this field. A reliable Convolution Neural Network with a graphical user interface is introduced in this study to support and enhance rehabilitation efforts. The spectrogram in the image-profiled sound is used to provide the optimum outcome and accuracy. This project aims to develop a neural network that can distinguish vowels between a normal person's and stroke patients' voices. In this proposed paper's result, an intelligent Convolution Neural Network system for Malay language vowel detection with high-performance accuracy is demonstrated with maximum accuracy was 92.96% with 20 epoch numbers and 6 batch size. This outcome showed that the proposed method, even using a simple network design, is still competitive compared with other methods. The proposed method is ideal for training and validating vowel recognition accuracy especially for stroke patients.