Automatic detection of Mild Cognitive Impairment using high-dimensional acoustic features in spontaneous speech
<|reference_start|>Automatic detection of Mild Cognitive Impairment using high-dimensional acoustic features in spontaneous speech: This study addresses the TAUKADIAL challenge, focusing on the classification of speech from people with Mild Cognitive Impairment (MCI) and neurotypical controls. We conducted three experiments comparing five machine-learning methods: Random Forests, Sparse Logistic Regression, k-Nearest Neighbors, Sparse Support Vector Machine, and Decision Tree, utilizing 1076 acoustic features automatically extracted using openSMILE. In Experiment 1, the entire dataset was used to train a language-agnostic model. Experiment 2 introduced a language detection step, leading to separate model training for each language. Experiment 3 further enhanced the language-agnostic model from Experiment 1, with a specific focus on evaluating the robustness of the models using out-of-sample test data. Across all three experiments, results consistently favored models capable of handling high-dimensional data, such as Random Forest and Sparse Logistic Regression, in classifying speech from MCI and controls.<|reference_end|>
@article{zhang2024automatic,
    title={Automatic detection of Mild Cognitive Impairment using high-dimensional
  acoustic features in spontaneous speech},
    author={Cong Zhang, Wenxing Guo, Hongsheng Dai},
    journal={arXiv preprint arXiv:2408.16732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16732},
    primaryClass={q-bio.NC cs.SD eess.AS q-bio.QM}
}