ECAPA-TDNN: A Model Based on Spontaneous Speech for MCI Detection

Mild Cognitive Impairment (MCI) is the early stage of Alzheimer’s Disease(AD). Using speech signals to diagnose MCI offers the advantages of low cost and high efficiency. In this paper, we propose a two-stage model for MCI recognition that employs ECAPA-TDNN as the feature extraction network. To address the challenging task of extracting AD-related features from speech, we introduce a one-dimensional Squeeze-Excitation (SE) block in the feature extraction network. This block adaptively reweights the input features to emphasize those that contribute significantly to the task while suppressing irrelevant or redundant ones. we utilize Chinese spontaneous speech from patients as our research data. After polling prediction, our accuracy in recognizing AD and MCI reaches 94.29% and 85.84%, respectively, demonstrating potential clinical applications.