PodAgent: A Comprehensive Framework for Podcast Generation
Authors: name: Yujia Xiao, name: Lei He, name: Haohan Guo, name: Fenglong Xie, name: Tan Lee

Existing 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 LLM-enhanced 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. Demo page:
https://podcast-agent.github.io/demo/. Source code:
https://github.com/yujxx/PodAgent.