arXiv:2606.19325v1 Announce Type: cross Abstract: Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations. We take a different approach. Our method, ScenA, conditions a text-to-audio flow-matching foundation model, pretrained on large-scale in-the-wild data, directly on multiple reference voices and a free-form natural language prompt that d
The increasing sophistication of large-scale in-the-wild audio data and recent advances in generative AI models enable the creation of highly realistic and complex audio scenes.
This breakthrough allows for generating audio that mirrors real-world conversational environments, adding a critical layer of realism currently missing from speech-only AI outputs.
AI-generated audio can now include nuanced ambient textures and distinct multi-speaker interactions, moving beyond simple clean vocal sequences to create rich, immersive soundscapes.
- · AI content creators
- · Video game industry
- · Virtual reality developers
- · Film and television production
- · Manual foley artists
- · Traditional audio production studios (for certain tasks)
- · Limited-capability audio synthesis platforms
AI-generated audio content will become significantly more realistic and indistinguishable from human-recorded scenes.
This improved realism could accelerate the development of highly immersive virtual environments and interactive AI agents.
The ethical and regulatory frameworks around synthetic audio will need to evolve rapidly to address issues like deepfakes and authentic identity perception.
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Read at arXiv cs.AI