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We evaluated whether fully automated linguistic analysis of speech tasks recorded via smartphone can remotely capture core clinical markers of HD severity. In this cross-sectional multicenter study across Czech and German sites, 53 participants, including 30 HD (9 pre-symptomatic, 3 prodromal, and 18 manifest) and 23 healthy controls, completed a smartphone-based speech assessment, including spontaneous monologue and fairy tale retelling for 7 consecutive days. Recordings were automatically transcribed and analyzed using natural language processing to derive 3 lexical and 3 syntactic features. Predictive models for clinical outcomes based on the Unified Huntington\u2019s Disease Rating Scale and cognitive scales were built using multivariate linear regression with cross-validation. Linguistic features predicted HD severity across multiple domains with high predictive performance, explaining up to 57% of the variance in cognitive performance, 63% in motor impairment, and 59% in functional capacity. A median of 6 days for monologue and 3 for retelling was sufficient to reach 90% of maximal predictive performance. Compared to controls, HD participants showed reduced vocabulary range and increased phrase repetition in both tasks (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05), with additional monologue-specific deficits in sentence length (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.018) and syntactic complexity (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.004). Fully automated analysis of smartphone-based language assessment can remotely quantify cognitive, motor, and functional impairment in HD, offering a scalable, low-burden digital biomarker for clinical trials and decentralized monitoring.\n                  <\/jats:p>","DOI":"10.1007\/s00702-026-03173-5","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:33:38Z","timestamp":1778754818000},"page":"2479-2490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Remote smartphone-based spoken language screening predicts clinical markers in Huntington\u2019s disease"],"prefix":"10.1007","volume":"133","author":[{"given":"Martin","family":"\u0160ubert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom\u00e1\u0161","family":"Kouba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tereza","family":"Tykalov\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Klemp\u00ed\u0159ov\u00e1","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alzbeta","family":"M\u00fchlb\u00e4ck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji\u0159\u00ed","family":"Klemp\u00ed\u0159","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Rusz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"issue":"6","key":"3173_CR1","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.jclinepi.2014.12.014","volume":"68","author":"PC Austin","year":"2015","unstructured":"Austin PC, Steyerberg EW (2015) The number of subjects per variable required in linear regression analyses. 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