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 speech-based 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.