Dual-branch hierarchical fusion network for voice-based classification of type 2 diabetes mellitus using multi-vowel acoustic features.

Problem: While voice-based analysis has emerged as a potential non-invasive approach for exploring acoustic alterations associated with Type 2 Diabetes Mellitus (T2DM), current approaches often fail to fully capture disease-relevant vocal patterns due to simplified recording protocols and insufficient feature modeling. Aim: This study aims to investigate the feasibility of voice-based classification of T2DM using a multi-feature fusion framework, leveraging sustained vowel phonations and integrating multiple acoustic feature types to enhance diagnostic performance. Methods: Voice recordings were collected from 378 participants, including T2DM patients and healthy controls. Each participant pronounced six Mandarin vowels, from which acoustic features-including Mel-frequency cepstral coefficients (MFCCs), glottal parameters, and eGeMAPS descriptors-were extracted. Neural networks, including convolutional and deep architectures, were applied to capture pathology-relevant segments. Vowel-level fusion modules aggregated features across vowels, and an attention-based hierarchical fusion mechanism combined the three feature types, adaptively emphasizing features most relevant to T2DM. Results: The proposed dual-branch hierarchical fusion model achieved 80.48% detection accuracy. Conclusion: These results suggest that sustained vowel phonations contain potentially informative acoustic patterns associated with T2DM status. However, further validation using independent cohorts is required before clinical application.