用多智能体系统将语音情绪转为安全可控的回应内容，实时响应且零风险。
Motion-to-Response Content Generation via Multi-Agent AI System with Real-Time Safety Verification
- 四智能体协作：识情绪、定策略、生参数、验安全，流程清晰。
- 情绪识别准确率73.2%，响应一致性89.4%，安全合规率100%。
- 适合儿童媒体、治疗应用和情感交互设备，可部署于本地设备。
本文提出一种多智能体人工智能系统，基于音频提取的情绪信号实时生成面向响应的媒体内容。与传统语音情绪识别侧重分类准确率不同，本方法强调将推断出的情绪状态转化为安全、适龄且可控的回应内容，通过专业化智能体构成的结构化流程实现。系统包含四个协同智能体：（1）基于CNN的声学特征提取情绪识别智能体；（2）情绪到响应模式映射的策略决策智能体；（3）生成媒体控制参数的内容生成智能体；（4）强制执行适龄性与刺激度约束的安全验证智能体。引入显式安全验证环路，在输出前过滤内容，确保符合预设规则。在公开数据集上的实验表明，系统实现73.2%的情绪识别准确率、89.4%的响应模式一致性以及100%的安全合规率，同时保持低于100ms的推理延迟，适用于本地部署。模块化架构提升可解释性与可扩展性，可应用于儿童相关媒体、治疗场景及情感响应智能设备。
原文摘要 · Abstract (English)
This paper proposes a multi-agent artificial intelligence system that generates response-oriented media content in real time based on audio-derived emotional signals. Unlike conventional speech emotion recognition studies that focus primarily on classification accuracy, our approach emphasizes the transformation of inferred emotional states into safe, age-appropriate, and controllable response content through a structured pipeline of specialized AI agents. The proposed system comprises four cooperative agents: (1) an Emotion Recognition Agent with CNN-based acoustic feature extraction, (2) a Response Policy Decision Agent for mapping emotions to response modes, (3) a Content Parameter Generation Agent for producing media control parameters, and (4) a Safety Verification Agent enforcing age-appropriateness and stimulation constraints. We introduce an explicit safety verification loop that filters generated content before output, ensuring compliance with predefined rules. Experimental results on public datasets demonstrate that the system achieves 73.2% emotion recognition accuracy, 89.4% response mode consistency, and 100% safety compliance while maintaining sub-100ms inference latency suitable for on-device deployment. The modular architecture enables interpretability and extensibility, making it applicable to child-adjacent media, therapeutic applications, and emotionally responsive smart devices.
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