Language-agnostic Speech Biomarker Exploration for Early Dementia Screening

Josh Ashik, Zongxing Xie, Ying Chen, Chenhan Xu, Huining Li

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Keywords: Speech Biomarker, Language-agnostic, Artificial Intelligence, Dementia Screening

TL;DR: We propose a language-agnostic screening pipeline for dementia detection in the early stage.

Abstract: 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 71\%. This cross-lingual transfer performance highlights the potential of our approach for scalable, language-independent dementia screening.

Track: 1. Digital Health Solutions (i.e. sensors and algorithms) for diagnosis, progress, and self-management

NominateReviewer: Josh Ashik

Submission Number: 109