Multimodal Depression

Multimodal depression detection research aims to automatically identify depression using various data sources like speech, video, and text, improving early diagnosis and intervention. Current efforts focus on developing sophisticated fusion models, often employing hierarchical attention networks, recurrent neural networks (like LSTMs), and large language models, to effectively integrate and interpret information across these modalities. These advancements leverage features such as acoustic landmarks, facial expressions, and linguistic patterns to enhance accuracy and interpretability, ultimately contributing to more reliable and clinically useful depression detection tools. The potential impact lies in enabling earlier and more efficient identification of depression, leading to improved mental healthcare access and treatment outcomes.