%Aigaion2 BibTeX export from Idiap Publications
%Friday 13 November 2026 04:58:35 PM

@INPROCEEDINGS{Hernandez_INTERSPEECH_2026,
                      author = {Hernandez, Abner and Yeo, Eunjung and Choi, Kwanghee and Li, Chin-Jou and Yue, Zhengjun and Das, Rohan Kumar and Rusz, Jan and Magimai-Doss, Mathew and Orozco-Arroyave, Juan Rafael and Arias-Vergara, Tom{\'{a}}s and Maier, Andreas and N{\"{o}}th, Elmar and Mortensen, David R. and Harwath, David and P{\'{e}}rez-Toro, Paula Andrea},
                    keywords = {cross-lingual, domain adaptation, Oral Diadochokinesis (DDK), Parkinson’s disease, self-supervised learning, ysarthria detection},
                    projects = {Idiap},
         mainresearchprogram = {AI for Life},
  additionalresearchprograms = {Human-AI Teaming},
                       title = {Adapting Self-Supervised Speech Representations for Cross-Lingual Dysarthria Detection in Parkinson's Disease},
                   booktitle = {Proceedings of Interspeech},
                        year = {2026},
                         doi = {10.21437/Interspeech.2026-773},
                    abstract = {The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode language-dependent structure that can confound dysarthria detection. We propose a representation-level language shift (LS) that aligns source-language self-supervised speech representations with the target-language distribution using centroid-based vector adaptation estimated from healthy control speech. We evaluate the approach on oral diadochokinesis recordings from Parkinson's disease speech datasets in Czech, German, and Spanish under both cross-lingual and multilingual settings. LS substantially improves sensitivity and F1 in cross-lingual settings, while yielding smaller but consistent gains in multilingual settings. Representation analysis further shows that LS reduces language identity in the embedding space, supporting the interpretation that LS removes language-dependent structure.}
}