用多个弱编码器提升心理咨询对话理解能力
WEE-Therapy: A Mixture of Weak Encoders Framework for Psychological Counseling Dialogue Analysis
- 引入弱编码器集成机制，融合多个轻量专业编码器
- 在情感识别等四类任务上性能显著提升，参数增长极少
- 适合心理AI辅助诊断、临床对话分析场景
计算心理学的发展需要能够深入理解咨询对话的AI工具。现有语音语言模型（AudioLLMs）通常依赖于在通用数据上预训练的单一语音编码器，难以捕捉复杂情绪和专业咨询技巧等特定领域特征。为此，我们提出WEE-Therapy，一种多任务音频语言模型，采用弱编码器集成（WEE）机制，补充主编码器以增强领域适应性。通过新颖的双路由策略，将稳定的数据无关领域知识与动态的数据相关专家选择相结合。在情感识别、技术分类、风险检测和摘要生成任务上的评估表明，WEE-Therapy在所有任务中均实现显著性能提升，且参数开销极小，展现出在人工智能辅助临床分析中的强大潜力。
原文摘要 · Abstract (English)
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.
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