WEE-Therapy：一种用于心理咨询对话分析的弱编码器混合框架
WEE-Therapy: A Mixture of Weak Encoders Framework for Psychological Counseling Dialogue Analysis
AI总结：
针对现有AudioLLM难以捕捉心理咨询对话领域特征的问题，提出融合弱编码器集成与双路由策略的多任务模型WEE-Therapy，以极低参数开销在多类咨询分析任务上取得显著性能提升。
摘要：
计算心理学的发展需要能够深度理解咨询对话的AI工具。现有的音频语言模型（AudioLLMs）通常依赖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、专业技巧等领域特定特征。为解决该问题，我们提出WEE-Therapy，这是一种融合弱编码器集成（WEE）机制的多任务AudioLLM，它在强大的基础编码器之外补充了一组轻量级专用编码器。一种新颖的双路由策略将稳定的、与数据无关的领域知识，与动态的、依赖数据的专家选择相结合。在情绪识别、技术分类、风险检测和摘要生成任务上的评估显示，WEE-Therapy以极低的参数开销在所有任务上实现了显著性能提升，展现出AI辅助临床分析的巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
