MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation

• URL: http://arxiv.org/abs/2606.11197v1
• Date: Mon, 20 Apr 2026 14:42:13 GMT
• Title: MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation
• Abstract summary: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention.<n>Most existing approaches rely on RNN-based architectures to model temporal information for depression estimation.<n>We introduce a memory-based feature augmentation method that enhances the representational capacity of GRU-extracted features.
• Score: 59.709198999959206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention, particularly in resource-constrained mental health settings. In recent years, deep learning has demonstrated impressive success across various domains, including affective computing and mental health assessment. Most existing approaches rely on RNN-based architectures (such as LSTM and GRU) to model temporal information for depression estimation. However, the extracted features often emphasize only a few adjacent speech segments, limiting their ability to capture long-range dependencies. To overcome this limitation, we introduce a memory-based feature augmentation method that enhances the representational capacity of GRU-extracted features. Rather than indiscriminately incorporating historical data, our memory bank is designed to selectively integrate two types of components in order to reduce redundancy and irrelevance: (1) historical temporal features that closely resemble the current GRU output, offering complementary contextual information; and (2) dynamic memory features identified based on feature variability, which capture behavioral and emotional fluctuations indicative of depressive symptoms. To effectively fuse the memory-augmented features with GRU outputs, we further design a Hierarchical Attention Fusion (HAF) module. Our method is evaluated on the widely used DAIC-WOZ and E-DAIC datasets, achieving state-of-the-art performance.

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