{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-method-for-analysis-of-patient-speech-in","title":"A Method for Analysis of Patient Speech in Dialogue for Dementia Detection","arxiv_id":"1811.09919","date":"2018-11-25","proceeding":null,"authors":["Saturnino Luz","Sofia de la Fuente","Pierre Albert"],"abstract":"We present an approach to automatic detection of Alzheimer's type dementia\nbased on characteristics of spontaneous spoken language dialogue consisting of\ninterviews recorded in natural settings. The proposed method employs additive\nlogistic regression (a machine learning boosting method) on content-free\nfeatures extracted from dialogical interaction to build a predictive model. The\nmodel training data consisted of 21 dialogues between patients with Alzheimer's\nand interviewers, and 17 dialogues between patients with other health\nconditions and interviewers. Features analysed included speech rate,\nturn-taking patterns and other speech parameters. Despite relying solely on\ncontent-free features, our method obtains overall accuracy of 86.5\\%, a result\ncomparable to those of state-of-the-art methods that employ more complex\nlexical, syntactic and semantic features. While further investigation is\nneeded, the fact that we were able to obtain promising results using only\nfeatures that can be easily extracted from spontaneous dialogues suggests the\npossibility of designing non-invasive and low-cost mental health monitoring\ntools for use at scale.","url_abs":"http://arxiv.org/abs/1811.09919v1","url_pdf":"http://arxiv.org/pdf/1811.09919v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-method-for-analysis-of-patient-speech-in","repo_url":"https://github.com/cran/vocaldia","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.09919","atlas_url":"https://app.syntology.ai/?focus=1811.09919","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}