Download PDFOpen PDF in browserReal-World Car Talk: A Digital Biomarker of Cognitive Trajectory10 pages•Published: April 19, 2026AbstractEarly detection of mild dementia is vital for timely intervention, yet most existing voice biomarker research relies on scripted, high-quality clinical recordings. Such settings fail to capture the acoustic variability of everyday life, limiting real-world applicability. This proof-of-concept study is the first to develop a voice-based biomarker for mild dementia using naturalistic in-vehicle audio data collected during routine driving. Audio recordings from 29 participants with sufficient speech content were processed to isolate speech segments through unsupervised clustering, followed by manual verification to ensure relevance. Speech embeddings were extracted using the Wav2Vec2 architecture and a multilayer perceptron classifier was trained and evaluated in a subject-level leave-one-subject-out (LOSO) framework. The model achieved an accuracy of 68.97%, precision of 75%, recall of 60%, and F1-score of 66.67%. These findings demonstrate the feasibility of deriving robust voice biomarkers from highly variable, noise-rich real-world audio. This work lays the groundwork for scalable, passive in-vehicle cognitive health monitoring, with future directions including larger datasets, multimodal integration, and longitudinal analysis for early dementia detection.Keyphrases: cognitive impairment detection, deep learning, in vehicle audio, mild dementia, passive monitoring, speech analysis, voice biomarkers In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 198-207.
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