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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T PodAgent: A Comprehensive Framework for Podcast Generation
%A Xiao, Yujia
%A He, Lei
%A Guo, Haohan
%A Xie, Feng-Long
%A Lee, Tan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F xiao-etal-2025-podagent
%X 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.
%R 10.18653/v1/2025.findings-acl.1226
%U https://aclanthology.org/2025.findings-acl.1226/
%U https://doi.org/10.18653/v1/2025.findings-acl.1226
%P 23923-23937

Markdown (Informal)

[PodAgent: A Comprehensive Framework for Podcast Generation](https://aclanthology.org/2025.findings-acl.1226/) (Xiao et al., Findings 2025)

• PodAgent: A Comprehensive Framework for Podcast Generation (Xiao et al., Findings 2025)

ACL

• Yujia Xiao, Lei He, Haohan Guo, Feng-Long Xie, and Tan Lee. 2025. PodAgent: A Comprehensive Framework for Podcast Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23923–23937, Vienna, Austria. Association for Computational Linguistics.