title: PodAgent: A Comprehensive Framework for Podcast Generation
summary: 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.
paper_authors._id: 67c6facdd8af5b36fd4b59cf
paper_authors.name: Yujia Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T16:08:12.490Z
paper_authors._id: 674836767b7151c3ff30f865
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jcwK5NW-efhCt8s2TE6vK.png
paper_authors.fullname: Yujia Xiao
paper_authors.type: user
paper_authors.user: Yogurt928
paper_authors._id: 67c6facdd8af5b36fd4b59d0
paper_authors.name: Lei He
paper_authors._id: 67c6facdd8af5b36fd4b59d1
paper_authors.name: Haohan Guo
paper_authors._id: 67c6facdd8af5b36fd4b59d2
paper_authors.name: Fenglong Xie
paper_authors._id: 67c6facdd8af5b36fd4b59d3
paper_authors.name: Tan Lee
paper_title: PodAgent: A Comprehensive Framework for Podcast Generation
paper_summary: 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.
paper_ai_keywords: Host-Guest-Writer multi-agent collaboration voice pool voice-role matching LLM-enhanced speech synthesis LLM-guided synthesis topic-discussion dialogue content voice-matching accuracy