1. Voice markers of neuropsychiatric disorders: assessing the generalizability performance of machine learning models
- Author
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Parola, Alberto, Rybner, Astrid, Jessen, Emil Trenckner, Larsen, Stine Nyhus, Mortensen, Marie Damsgaard, Simonsen, Arndis, Zhou, Yuan, Koelkebeck, Katja, Bliksted, Vibeke, and Fusaroli, Riccardo
- Subjects
Linguistics ,Psychology ,Machine learning ,Natural Language Processing ,Speech recognition - Abstract
This research explores the potential of machine learning (ML) in identifying vocal markers for schizophrenia. While previous research showed that voice-based ML models can accurately predict schizophrenia diagnosis and symptoms, it is unclear to what extent such ML markers generalize to different clinical subpopulations and languages: the assessment of generalization performance is however crucial for testing their clinical applicability.We systematically examined voice-based ML model performance on a large cross-linguistic dataset (3 languages: Danish, German, Chinese). Employing a rigorous pipeline to minimize overfitting, including cross-validated training sets and multilingual models, we assessed generalization on participants with schizophrenia and controls speaking the same or different languages. Model performance was comparable to state-of-the art findings (F1-score ~ 0.75) within the same language; however, models did not generalize well - showing a substantial decrease - when tested on new languages, and the performance of multilingual models was also generally low (F1-score ~ 0.50).
- Published
- 2024