SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development
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Abstract (arXiv)
High-quality speech dialogue datasets are crucial for Speech-LLM development, yet existing acquisition methods face significant limitations. Human recordings incur high costs and privacy concerns, while synthetic approaches often lack conversational authenticity. To address these challenges, we introduce textscSpeechDialogueFactory, a production-ready framework for generating natural speech dialogues efficiently. Our solution employs a comprehensive pipeline including metadata generation, dialogue scripting, paralinguistic-enriched utterance simulation, and natural speech synthesis with voice cloning. Additionally, the system provides an interactive UI for detailed sample inspection and a high-throughput batch synthesis mode. Evaluations show that dialogues generated by our system achieve a quality comparable to human recordings while significantly reducing production costs. We release our work as an open-source toolkit, alongside example datasets available in English and Chinese, empowering researchers and developers in Speech-LLM research and development.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 31 Mar 2025.
Builds on
30 of 39 references.
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18 more
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