CAtCh: Cognitive Assessment through Cookie Thief
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912418217590784 |
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| author | Colonel, Joseph T Hagler, Carolyn Wismer, Guiselle Curtis, Laura Becker, Jacqueline Wisnivesky, Juan Federman, Alex Pandey, Gaurav |
| author_facet | Colonel, Joseph T Hagler, Carolyn Wismer, Guiselle Curtis, Laura Becker, Jacqueline Wisnivesky, Juan Federman, Alex Pandey, Gaurav |
| contents | Several machine learning algorithms have been developed for the prediction of Alzheimer's disease and related dementia (ADRD) from spontaneous speech. However, none of these algorithms have been translated for the prediction of broader cognitive impairment (CI), which in some cases is a precursor and risk factor of ADRD. In this paper, we evaluated several speech-based open-source methods originally proposed for the prediction of ADRD, as well as methods from multimodal sentiment analysis for the task of predicting CI from patient audio recordings. Results demonstrated that multimodal methods outperformed unimodal ones for CI prediction, and that acoustics-based approaches performed better than linguistics-based ones. Specifically, interpretable acoustic features relating to affect and prosody were found to significantly outperform BERT-based linguistic features and interpretable linguistic features, respectively. All the code developed for this study is available at https://github.com/JTColonel/catch. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CAtCh: Cognitive Assessment through Cookie Thief Colonel, Joseph T Hagler, Carolyn Wismer, Guiselle Curtis, Laura Becker, Jacqueline Wisnivesky, Juan Federman, Alex Pandey, Gaurav Machine Learning Artificial Intelligence Sound Audio and Speech Processing Several machine learning algorithms have been developed for the prediction of Alzheimer's disease and related dementia (ADRD) from spontaneous speech. However, none of these algorithms have been translated for the prediction of broader cognitive impairment (CI), which in some cases is a precursor and risk factor of ADRD. In this paper, we evaluated several speech-based open-source methods originally proposed for the prediction of ADRD, as well as methods from multimodal sentiment analysis for the task of predicting CI from patient audio recordings. Results demonstrated that multimodal methods outperformed unimodal ones for CI prediction, and that acoustics-based approaches performed better than linguistics-based ones. Specifically, interpretable acoustic features relating to affect and prosody were found to significantly outperform BERT-based linguistic features and interpretable linguistic features, respectively. All the code developed for this study is available at https://github.com/JTColonel/catch. |
| title | CAtCh: Cognitive Assessment through Cookie Thief |
| topic | Machine Learning Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.06603 |