Pre-trained Feature Fusion and Matching for Mild Cognitive Impairment Detection

Effective diagnosis of Mild Cognitive Impairment (MCI), a preclinical stage of cognitive decline, is significant for delaying disease progression.While most current spontaneous speechbased diagnostic methods focus on English speech, the Interspeech 2024 TAUKADIAL Challenge proposed an innovative research direction to develop a language-agnostic approach to diagnose MCI.This paper proposes an MCI diagnosis method by analyzing and combining linguistic and acoustic features using the bilingual Chinese-English speech dataset provided by the challenge.We employed a pre-trained multilingual model and expressivity encoder to extract language-agnostic speech features.To overcome the challenges of data scarcity and language diversity, we implemented data augmentation and alignment to enhance the model's generalization.Our approach achieved 77.5% accuracy, demonstrating its effectiveness and potential on cross-lingual data.