Combining Acoustic Feature Sets for Detecting Mild Cognitive Impairment in the Interspeech'24 TAUKADIAL Challenge

Shared tasks or challenges provide valuable opportunities for the machine learning community, as they offer a chance to compare the performance of machine learning approaches without peeking (due to the hidden test set). We present the approach of our team for the Interspeech'24 TAUKADIAL Challenge, where the task is to distinguish patients of Mild Cognitive Impairment (MCI) from healthy controls based on their speech. Our workflow focuses entirely on the acoustics, mixing standard feature sets (ComParE functionals and wav2vec2 embeddings) and custom attributes focusing on the amount of silent and filled pause segments. By training dedicated SVM classifiers on the three speech tasks and combining the predictions over the different speech tasks and feature sets, we obtained F1 values of up to 0.76 for the MCI identification task using cross-validation, while our RMSE scores for the MMSE estimation task were as low as 2.769 (cross-validation) and 2.608 (test).