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Tomoko Tateyama

2 accepted papers

2026

Dynamic Summary Generation for Interpretable Multimodal Depression Detection

ICASSP 2026poster

Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this challenge, we propose a coarse-to-fine, multi-stage framework that leverages large language models (LLMs) for accurate and interpretable detection. The p…

Cited by 0SourcePDFScholar
2025

Enhanced Multimodal Depression Detection With Emotion Prompts

ICASSP 2025accepted

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion p…

Cited by 0SourceScholar