| title | Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment |
|---|---|
| authors | Anisha Pattanayak, Hanie Kang, Huang-Cheng Chou, Shrikanth Narayanan, Sudarsana Reddy Kadiri |
| date | 2026-07-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.02920 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Significant disparities exist in the diagnosis and clinical presentation of depression across different linguistic populations. Speech-based depression detection performs well monolingually, but cross-lingual generalization remains an open challenge. A key reason is that prior work uses segment-level random splits without speaker grouping, leading to identity leakage that inflates reported metrics. We propose CLeaD, a supervised contrastive alignment framework that maps WavLM embeddings from English and Mandarin into a shared clinical space, without parallel data or target-language fine-tuning. Evaluating 52 Mandarin speakers, contrastive alignment modestly outperforms the baseline (F1: 0.640 vs. 0.622) under leave-one-speaker-out evaluation. It also improves depressed-class recall at intermediate layers (7-8), though the small test set limits generalizability. Two findings remain robust: model scaling degrades cross-lingual performance while improving monolingual English, and speaker identity leakage artificially inflated previously reported Mandarin F1 scores to 0.954, an artifact we reproduce and quantify. |
cite
@misc{pattanayak2026layer,
title={Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment},
author={Anisha Pattanayak and Hanie Kang and Huang-Cheng Chou and Shrikanth Narayanan and Sudarsana Reddy Kadiri},
year={2026},
eprint={2607.02920},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.02920}
}