LLM Detects Burnout Signals Hidden in Our Workplace Stories

Can AI Hear Your Mental Health? Turning Everyday Talk into Clinical Insights

In this episode, we explore how researchers are building the "ears" of AI to detect signs of depression and anxiety hidden in spoken language. We break down the creation of DEPAC, a massive new audio dataset designed to overcome the limitations of traditional diagnosis by using diverse speech tasks—from describing a picture to simple phoneme sounds. The paper is (link [https://arxiv.org/pdf/2306.12443]): DEPAC: a Corpus for Depression and Anxiety Detection from Speech [https://arxiv.org/pdf/2306.12443] Whether you’re a data scientist interested in digital biomarkers or a psychology enthusiast curious about how acoustic features like pitch and pauses can predict clinical scores, this episode offers a fascinating look at the intersection of crowdsourcing, machine learning, and mental health diagnostics.

LLM Detects Burnout Signals Hidden in Our Workplace Stories

In this episode, we unpack research showing how LLM can understand human wellbeing. The paper is (link [https://riko.io/from-narratives-to-numbers.pdf]): From Narratives to Numbers: Evaluating a Large Language Model (LLM) for Transforming Workplace Interview Narratives into Numerical Wellbeing Indicators [https://riko.io/from-narratives-to-numbers.pdf] Whether you’re curious about AI in research, workplace wellbeing, or the future of mixed-methods analysis, this episode offers a clear, accessible dive into how LLMs are learning to hear the emotional signals we don’t always say out loud.