Segmental and Suprasegmental Speech Foundation Models for Classifying Cognitive Risk Factors: Evaluating Out-of-the-Box Performance.

Abstract

Speech foundation models are remarkably successful in various consumer applications, prompting their extension to clinical use-cases. This is challenged by small clinical datasets, which precludes effective fine-tuning. We tested the efficacy of two models to classify participants by segmental (Wav2Vec2.0) and suprasegmental (Trillsson) speech analysis windows. Analysis at both time scales has shown differences in the context of cognitive decline. Speakers were classified as healthy controls (HC), Amyloid-β+ (Aβ+), mild cognitive impairment (MCI), or dementia. A subset of W2V2 and Trillsson representations showed large effect size between HC and each risk factor. Cross-validation showed W2V2 consistently outperforms Trillsson. Mean macro-F1 of 54.1%, 63.5%, and 72.0% in were found for classifying Aβ+, MCI, and dementia from HC. Repeatability of Trillsson and W2V2 showed intraclass correlations of 0.30 and 0.41. Reliability of such models must be enhanced for clinical speech analysis and longitudinal tracking.