Developing a Hyperkinetic Dysarthria Speech Classification System using Residual Learning

Hyperkinetic dysarthria abnormalities are exceedingly widespread across the worldwide populace. One of the most effective methodologies for disordered audio speech identification is classical deep learning technique. However, the efficiency of classification is impacted by the limitations imposed by the quality of the training dataset, such as expense and limited resources, data imbalance, and data annotation difficulties. Accordingly, we suggest implementing a residual learning framework (ResN et architecture) with various conditions and hyperparameters (batch size and learning rate) that improve the training process of deeper networks compared to earlier approaches. Pre-emphasis, windowing, and lifting techniques are applied to improve the quality of Mel-Frequency Cepstral Coefficients. In addition, we have introduced the hyperkinetic dysarthria speech dataset in the Russian language, consisting of 4 hours and 4 minutes of recorded speech. The empirical findings demonstrate that ResNet40 has an overall validation accuracy of 86.7^%, surpassing other research models. It is anticipated that it will be recognized as an alternative diagnostic instrument for physicians in the future as research continues to progress.