Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment
Summary
This paper introduces CLeaD, a supervised contrastive alignment framework for cross-lingual depression detection from speech using WavLM embeddings. It reveals that previous results were inflated due to speaker identity leakage and achieves modest improvements on Mandarin speakers.
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Paper page - Layer-wise Cross-Lingual Depression Detection from Speech: Analysis with Contrastive Alignment
Source: https://huggingface.co/papers/2607.02920
Abstract
A supervised contrastive alignment framework maps WavLM embeddings from English and Mandarin into a shared clinical space for depression detection, addressing cross-lingual generalization challenges and revealing performance artifacts caused by speaker identity leakage.
Significant disparities exist in the diagnosis and clinical presentation of depression across different linguistic populations. Speech-based depression detection performs well monolingually, butcross-lingual generalizationremains 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, asupervised contrastive alignmentframework that mapsWavLM embeddingsfrom English andMandarininto a shared clinical space, without parallel data or target-language fine-tuning. Evaluating 52Mandarinspeakers, contrastive alignment modestly outperforms the baseline (F1: 0.640 vs. 0.622) underleave-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 improvingmonolingual English, andspeaker identity leakageartificially inflated previously reportedMandarinF1 scores to 0.954, an artifact we reproduce and quantify.
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