Multi-Scale Cooperative Vowel Attention Strategy for Voice-Based Type 2 Diabetes Mellitus Detection

Non-invasive diagnostic technologies for type 2 diabetes mellitus (T2DM) have emerged to address the limitations inherent in conventional invasive detection methods. Blood glucose levels may modulate laryngeal neuromuscular activity, thereby supporting the investigation of voice-based T2DM detection as a potential approach. However, methodological constraints in data acquisition and analytical limitations in extant studies have impeded substantive progress in voice-based T2DM detection. To systematically investigate voice-based T2DM detection, we conducted a comprehensive acoustic analysis of 408 participants, comprising both non-diabetic (ND) controls and T2DM patients. Specifically, we collected sustained vowel phonations and standardized short sentences from each participant to develop the multi-scale cooperative vowel attention strategy (MsCVA). This architecture integrates two complementary modules: the vowel stream analysis (VSA) module and the speech stream analysis (SSA) module to extract discriminative feature representations. Through multimodal feature fusion, MsCVA achieved an accuracy of 80.12% in voice-based T2DM detection.