{
  "id": 90,
  "url": "https://arxiv.org/abs/2607.08409v1",
  "title": "When Synthetic Speech Is All You Have: Better Call GRPO",
  "summary": "LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech t",
  "authors": "Shashi Kumar, Yanis Labrak, Hasindri Watawana, Sergio Burdisso, Esaú Villatoro-Tello, Kadri Hacioğlu et al.",
  "category": "research",
  "topics": "regulation,privacy-surveillance,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-09T12:34:56.000Z",
  "fetched_at": "2026-07-14T14:14:15.666Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/90",
  "original_url": "https://arxiv.org/abs/2607.08409v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}