Papers by Julien Epps

2 papers
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection (2024.emnlp-main)

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Challenge: Large language models (LLMs) are used for depression detection but their application remains unexplored.
Approach: They propose to integrate acoustic speech information into LLMs for depression detection by integrating aural landmarks into the framework.
Outcome: The proposed method adds critical dimensions to speech transcripts and provides insights into the unique speech patterns of individuals.
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information (2025.findings-acl)

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Challenge: Large Language Models (LLMs) have been extensively utilized for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input.
Approach: They propose a system that leverages speech timing features for depression detection and reliable confidence estimation.
Outcome: The proposed system outperforms text-based RAG systems in depression detection and confidence estimation.

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