# EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation

arXiv: https://arxiv.org/abs/2605.22286 · HTML: https://mlsanity.com/paper/2605.22286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 24 of 24)

- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling](https://mlsanity.com/paper/2405.16433/) (2024)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory](https://mlsanity.com/paper/2407.03103/) (2024)
- [ChatCounselor: A Large Language Models for Mental Health Support](https://mlsanity.com/paper/2309.15461/) (2023)
- [MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance](https://mlsanity.com/paper/2503.13509/) (2025)
- [PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling](https://mlsanity.com/paper/2412.13660/) (2024)
- [Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations](https://mlsanity.com/paper/2111.06647/) (2021)
- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection](https://mlsanity.com/paper/2402.13276/) (2024)
- [MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance](https://mlsanity.com/paper/2504.13211/) (2025)
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- [Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations](https://mlsanity.com/paper/2504.13955/) (2025)
- [Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions](https://mlsanity.com/paper/2508.20764/) (2025)
- [PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling](https://mlsanity.com/paper/2409.20243/) (2024)
- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues](https://mlsanity.com/paper/2504.21800/) (2025)
- [HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered Therapy Using LLM Agents](https://mlsanity.com/paper/2502.05982/) (2025)
- [KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy](https://mlsanity.com/paper/2502.05651/) (2025)
- [Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish](https://mlsanity.com/paper/2502.08458/) (2025)
- [PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor](https://mlsanity.com/paper/2601.01802/) (2026)
- [Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.01315/) (2025)

## Abstract (arXiv)

Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on the eight-item Patient Health Questionnaire (PHQ-8), a self-report measure of depression severity, from counseling transcripts. Clinical-based methods rely mainly on large language model (LLM) inference to obtain structured session-level assessments, but these assessments provide limited information about which utterances support each score. Training-based methods train predictors directly on sentences or their semantic embeddings and preserve local conversational detail, but must learn clinical structure from limited labeled transcripts. Integrating a small set of clinical feature scores with a long sequence of high-dimensional utterance representations is challenging, as simple fusion can overemphasize dialogue evidence and fail to link clinical features to supporting utterances. Combining structured clinical assessments with sentence-level semantics, we propose EmoTrack, which jointly encodes clinical feature scores and utterance representations to predict the total PHQ-8 score. For longitudinal assessment across successive sessions, the model can also incorporate a compressed representation of the preceding session as optional memory. On the real-world DAIC-WOZ benchmark, EmoTrack reduces mean absolute error from 2.8234 to 2.4708 relative to the strongest evaluated baseline. Across five settings covering single-session estimation, cross-dataset transfer without adaptation, and longitudinal assessment, it reduces normalized aggregate error by approximately 6.1% relative to the strongest baseline.