PodAgent: A Comprehensive Framework for Podcast Generation

Existing automatic audio generation methods struggle to generate podcast-like audio programs effectively.The key challenges lie in in-depth content generation, appropriate and expressive voice production.This paper proposed PodAgent, a comprehensive framework for creating audio programs.PodAgent 1) generates informative topic-discussion content by designing a Host-Guest-Writer multi-agent collaboration system, 2) builds a voice pool for suitable voice-role matching and 3) utilizes LLMenhanced speech synthesis method to generate expressive conversational speech.Given the absence of standardized evaluation criteria for podcast-like audio generation, we developed comprehensive assessment guidelines to effectively evaluate the model's performance.Experimental results demonstrate PodAgent's effectiveness, significantly surpassing direct GPT-4 generation in topic-discussion dialogue content, achieving an 87.4% voice-matching accuracy, and producing more expressive speech through LLM-guided synthesis.