PodAgent: A Comprehensive Framework for Podcast Generation
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.
67c6facdd8af5b36fd4b59cf
Yujia Xiao
claimed_verified
2025-03-04T16:08:12.490Z
674836767b7151c3ff30f865
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jcwK5NW-efhCt8s2TE6vK.png
Yujia Xiao
user
Yogurt928
67c6facdd8af5b36fd4b59d0
Lei He
67c6facdd8af5b36fd4b59d1
Haohan Guo
67c6facdd8af5b36fd4b59d2
Fenglong Xie
67c6facdd8af5b36fd4b59d3
Tan Lee
PodAgent: A Comprehensive Framework for Podcast Generation
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.
PodAgent, a multi-agent framework, effectively generates podcast-like content and expressive speech through topic-discussion, voice-matching, and LLM-enhanced synthesis.
Host-Guest-Writer multi-agent collaboration system voice pool voice-role matching LLM-enhanced speech synthesis