Motion-to-Response Content Generation via Multi-Agent AI System with Real-Time Safety Verification

TL;DR

This paper presents a multi-agent AI system that generates real-time response media content from audio emotional cues, ensuring safety and appropriateness through a structured, safety-verified pipeline.

Contribution

It introduces a novel multi-agent architecture with explicit safety verification for real-time, response-oriented media content generation from emotional signals.

Findings

Achieved 73.2% emotion recognition accuracy

Reached 89.4% response mode consistency

Ensured 100% safety compliance

Abstract

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.