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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.14354 |
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| _version_ | 1866914477034700800 |
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| author | Yeh, Hsiang-Chen Sun, Luqi Mahapatra, Aurosweta Chandra, Shreeram Suresh Provost, Emily Mower Sisman, Berrak |
| author_facet | Yeh, Hsiang-Chen Sun, Luqi Mahapatra, Aurosweta Chandra, Shreeram Suresh Provost, Emily Mower Sisman, Berrak |
| contents | This study investigates whether speech-based depression detection models learn depression-related acoustic biomarkers or instead rely on speaker identity cues. Using the DAIC-WOZ dataset, we propose a data-splitting strategy that controls speaker overlap between training and test sets while keeping the training size constant, and evaluate three models of varying complexity. Results show that speaker overlap significantly boosts performance, whereas accuracy drops sharply on unseen speakers. Even with a Domain-Adversarial Neural Network, a substantial performance gap remains. These findings indicate that depression-related features extracted by current speech models are highly entangled with speaker identity. Conventional evaluation protocols may therefore overestimate generalization and clinical utility, highlighting the need for strictly speaker-independent evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14354 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Who is Speaking or Who is Depressed? A Controlled Study of Speaker Leakage in Speech-Based Depression Detection Yeh, Hsiang-Chen Sun, Luqi Mahapatra, Aurosweta Chandra, Shreeram Suresh Provost, Emily Mower Sisman, Berrak Audio and Speech Processing This study investigates whether speech-based depression detection models learn depression-related acoustic biomarkers or instead rely on speaker identity cues. Using the DAIC-WOZ dataset, we propose a data-splitting strategy that controls speaker overlap between training and test sets while keeping the training size constant, and evaluate three models of varying complexity. Results show that speaker overlap significantly boosts performance, whereas accuracy drops sharply on unseen speakers. Even with a Domain-Adversarial Neural Network, a substantial performance gap remains. These findings indicate that depression-related features extracted by current speech models are highly entangled with speaker identity. Conventional evaluation protocols may therefore overestimate generalization and clinical utility, highlighting the need for strictly speaker-independent evaluation. |
| title | Who is Speaking or Who is Depressed? A Controlled Study of Speaker Leakage in Speech-Based Depression Detection |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2604.14354 |