ScenA conditions a text-to-audio foundation model on multiple reference voices and a natural language scene prompt to generate realistic multi-speaker conversations. It addresses the 'Reference Shortcut' issue by using a high-noise-biased training schedule, ensuring speaker assignment relies on text prompts rather than acoustic similarity. Evaluated on CoVoMix2-Dialogue, Scen- A outperforms existing systems in speaker-binding and produces rich, naturalistic audio with overlapping speech and ambient noise.