WEE-Therapy: A Mixture of Weak Encoders Framework for Psychological Counseling Dialogue Analysis
This work addresses the need for AI-assisted clinical analysis in computational psychology, offering an incremental improvement over existing methods by enhancing domain-specific feature capture.
The paper tackled the problem of AI tools struggling to capture domain-specific features like emotions and techniques in psychological counseling dialogues by proposing WEE-Therapy, a multi-task AudioLLM with a Weak Encoder Ensemble mechanism, which achieved significant performance gains across emotion recognition, technique classification, risk detection, and summarization tasks with minimal parameter overhead.
The advancement of computational psychology requires AI tools capable of deeply understanding counseling dialogues. Existing audio language models (AudioLLMs) often rely on single speech encoders pre-trained on general data, struggling to capture domain-specific features like complex emotions and professional techniques. To address this, we propose WEE-Therapy, a multi-task AudioLLM incorporating a Weak Encoder Ensemble (WEE) mechanism. This supplements a powerful base encoder with a pool of lightweight, specialized encoders. A novel dual-routing strategy combines stable, data-independent domain knowledge with dynamic, data-dependent expert selection. Evaluated on emotion recognition, technique classification, risk detection, and summarization, WEE-Therapy achieves significant performance gains across all tasks with minimal parameter overhead, demonstrating strong potential for AI-assisted clinical analysis.