EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes

TL;DR

EmoTrack is a novel depression prediction framework that leverages counseling transcripts and longitudinal data to improve PHQ-8 severity estimation across different session regimes.

Contribution

The paper introduces LongCounsel dataset and EmoTrack framework, enhancing depression tracking by combining LLM signals with session memory for robustness.

Findings

EmoTrack reduces MAE by 13.5% on single-session benchmarks.

It performs competitively on multi-session longitudinal data.

The approach effectively integrates session history for improved predictions.

Abstract

Text-based counseling is an important interface for AI mental-health support, where transcripts may be used to monitor depression severity and flag sessions requiring timely human review. However, robust PHQ-8 prediction across session regimes remains challenging: fine-tuning-based methods can exploit richer supervision but may generalize poorly under data scarcity, while prompt-based LLM methods are data-efficient but usually treat each transcript holistically and provide limited support for longitudinal context. We study robust depression tracking from counseling transcripts across single-session and multi-session regimes. We introduce LongCounsel, a multi-session counseling dataset with session-level PHQ-8 supervision for evaluating repeated-session tracking under partial symptom disclosure and cross-session continuity. We further propose EmoTrack, a PHQ-8 prediction framework that combines LLM-extracted clinical signals with frozen turn-level semantic embeddings and trains symptom-specific predictors over the resulting transcript representation. When prior sessions are available, EmoTrack can further incorporate them through compact cross-session memory. Experiments on LongCounsel and DAIC-WOZ show that EmoTrack achieves a clear gain on the real single-session benchmark, including a 13.5% relative MAE reduction over the strongest DAIC-WOZ baseline, and remains competitive with the strongest longitudinal baseline on LongCounsel.