{
  "id": 567,
  "url": "https://arxiv.org/abs/2606.26901v1",
  "title": "SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages",
  "summary": "Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare context remains largely unknown. In this study, we first conduct the systematic audit of ASR performance on real-world psychiatric interview data spanning Kannada, Hindi and Indian English, comparing eight state-of-the-art models including IndicWhisper, WhisperLargeV3, Sarvam, GoogleS2T, Gemma3n, OmniLingual, Vaani, and Gemini.",
  "authors": "Subham Kumar, Prakrithi Shivaprakash, Abhishek Manoharan, Astut Kurariya, Diptadhi Mukherjee, Prabhat Chand et al.",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": "google",
  "regions": "india",
  "published_at": "2026-06-25T11:34:07.000Z",
  "fetched_at": "2026-07-14T14:14:37.248Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/567",
  "original_url": "https://arxiv.org/abs/2606.26901v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}