MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation
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 meth...
| Authors | Xuzhi Wang, Xinran Wu, Ziping Zhao et al. |
|---|---|
| Primary category | eess.AS |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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