@article{ashik_xie_chen_xu_li_2025, title={Language-Agnostic Speech Biomarker Exploration for Early Dementia Screening}, volume={11}, DOI={10.1109/bsn66969.2025.11337743}, abstractNote={Early dementia detection is a global healthcare priority in diverse populations. In this study, we propose a language-agnostic screening pipeline for dementia detection in the early stage. First, we use speaker diarization to isolate the speech of the target subject from a conversational recording. From the extracted speech segments, we derive a set of acoustic features (e.g., spectral centroid, pitch mean, mel-frequency cepstral coefficients) and linguistic features (e.g., normalized tone contrast, articulation clarity coefficient, articulatory effort coefficient). These features are used to train a ResNet-based binary classifier to distinguish between Healthy Controls (HC) and individuals with Mild Cognitive Impairment (MCI). We evaluated the trained model on a held-out test set comprising speakers of previously unseen languages, achieving an accuracy of 70%. This cross-lingual transfer performance highlights the potential of our approach for scalable, language-independent dementia screening.}, author={Ashik, Josh and Xie, Zongxing and Chen, Ying and Xu, Chenhan and Li, Huining}, year={2025}, month={Nov} }