{"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/spoken-language-biomarkers-for-detecting","title":"Spoken Language Biomarkers for Detecting Cognitive Impairment","arxiv_id":"1710.07551","date":"2017-10-20","proceeding":null,"authors":["Tuka Alhanai","Rhoda Au","James Glass"],"abstract":"In this study we developed an automated system that evaluates speech and\nlanguage features from audio recordings of neuropsychological examinations of\n92 subjects in the Framingham Heart Study. A total of 265 features were used in\nan elastic-net regularized binomial logistic regression model to classify the\npresence of cognitive impairment, and to select the most predictive features.\nWe compared performance with a demographic model from 6,258 subjects in the\ngreater study cohort (0.79 AUC), and found that a system that incorporated both\naudio and text features performed the best (0.92 AUC), with a True Positive\nRate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow\ntest > 0.05). We also found that decreasing pitch and jitter, shorter segments\nof speech, and responses phrased as questions were positively associated with\ncognitive impairment.","url_abs":"http://arxiv.org/abs/1710.07551v1","url_pdf":"http://arxiv.org/pdf/1710.07551v1.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":"spoken-language-biomarkers-for-detecting","repo_url":"https://github.com/talhanai/asru2017-method","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.07551","atlas_url":"https://app.syntology.ai/?focus=1710.07551","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}