Bidirectional Multimodal Block-Recurrent Transformers for Depression Detection

Depression, as a prevalent and severe psychological disorder, has become a burden to individuals, families and societies all over the world. Recently, some deep learning methods have been introduced for depression detection and achieved promising performance on a number of public datasets. Most of them rely on Long Short-Term Memory (LSTM) or Transformer to model multimodal time series data used for depression detection which fail in filtering noisy information within multiple modalities. Motivated by block-recurrent transformers, which has a strong ability to filter noisy information among single-modal time series, we propose novel block-recurrent transformers, called Bidirectional Multimodal Block-Recurrent Transformers (BMBRT), for multimodal data analysis and apply it to depression detection. BMBRT is extended from the block-recurrent transformers by introducing multimodal data enhancement module to obtain complementary information across modalities and designing a new multi-block transformer module for noise filtering. Experiments on three publicly available depression detection datasets show that our proposed method significantly outperforms current state-of-the-art methods.