Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements
Journal:
arXiv
Published Date:
Jun 11, 2025
Abstract
Prolonged Exposure (PE) therapy is an effective treatment for post-traumatic
stress disorder (PTSD), but evaluating therapist fidelity remains
labor-intensive due to the need for manual review of session recordings. We
present a method for the automatic temporal localization of key PE fidelity
elements -- identifying their start and stop times -- directly from session
audio and transcripts. Our approach fine-tunes a large pre-trained
audio-language model, Qwen2-Audio, using Low-Rank Adaptation (LoRA) to process
focused 30-second windows of audio-transcript input. Fidelity labels for three
core protocol phases -- therapist orientation (P1), imaginal exposure (P2), and
post-imaginal processing (P3) -- are generated via LLM-based prompting and
verified by trained raters. The model is trained to predict normalized boundary
offsets using soft supervision guided by task-specific prompts. On a dataset of
313 real PE sessions, our best configuration (LoRA rank 8, 30s windows)
achieves a mean absolute error (MAE) of 5.3 seconds across tasks. We further
analyze the effects of window size and LoRA rank, highlighting the importance
of context granularity and model adaptation. This work introduces a scalable
framework for fidelity tracking in PE therapy, with potential to support
clinician training, supervision, and quality assurance.
