Deep learning-based classification of speech disorder in stroke and hearing impairment.

Background and objective: Speech disorders can arise from various causes, including congenital conditions, neurological damage, diseases, and other disorders. Traditionally, medical professionals have used changes in voice to diagnose the underlying causes of these disorders. With the advancement of artificial intelligence (AI), new possibilities have emerged in this field. However, most existing studies primarily focus on comparing voice data between normal individuals and those with speech disorders. Research that classifies the causes of these disorders within the abnormal voice data, attributing them to specific etiologies, remains limited. Therefore, our objective was to classify the specific causes of speech disorders from voice data resulting from various conditions, such as stroke and hearing impairments (HI).

Methods: We experimentally developed a deep learning model to analyze Korean speech disorder voice data caused by stroke and HI. Our goal was to classify the disorders caused by these specific conditions. To achieve effective classification, we employed the ResNet-18, Inception V3, and SEResNeXt-18 models for feature extraction and training processes.

Results: The models demonstrated promising results, with area under the curve (AUC) values of 0.839 for ResNet-18, 0.913 for Inception V3, and 0.906 for SEResNeXt-18, respectively.

Conclusions: These outcomes suggest the feasibility of using AI to efficiently classify the origins of speech disorders through the analysis of voice data.