Bias and Fairness in Self-Supervised Acoustic Representations for Cognitive Impairment Detection
Speech-based detection of cognitive impairment (CI) offers a promising non-invasive approach for early diagnosis, yet performance disparities across demographic and clinical subgroups remain underexplored, raising concerns around fairness and generalizability. This study presents a systematic bias analysis of acoustic-based CI and depression classification using the DementiaBank Pitt Corpus. We compare traditional acoustic features (MFCCs, eGeMAPS) with contextualized speech embeddings from Wav2Vec 2.0 (W2V2), and evaluate classification performance across gender, age, and depression-status subgroups. For CI detection, higher-layer W2V2 embeddings outperform baseline features (UAR up to 8...
| Authors | Kashaf Gulzar, Korbinian Riedhammer, Elmar Nöth et al. |
|---|---|
| Primary category | eess.AS |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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