# ASR Under the Stethoscope: Evaluating Biases in Clinical Speech Recognition across Indian Languages

arXiv: https://arxiv.org/abs/2512.10967 · HTML: https://mlsanity.com/paper/2512.10967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 30 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages](https://mlsanity.com/paper/2403.01926/) (2024)
- [IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages](https://mlsanity.com/paper/2208.11761/) (2022)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [Svarah: Evaluating English ASR Systems on Indian Accents](https://mlsanity.com/paper/2305.15760/) (2023)
- [A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos](https://mlsanity.com/paper/2307.10587/) (2023)
- [DENOASR: Debiasing ASRs through Selective Denoising](https://mlsanity.com/paper/2410.16712/) (2024)
- [Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology](https://mlsanity.com/paper/2508.18288/) (2025)

## Built on by (most cited first, 3 of 3)

- [Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR](https://mlsanity.com/paper/2608.10670/) (2026)
- [RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery](https://mlsanity.com/paper/2603.16411/) (2026)
- [From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation](https://mlsanity.com/paper/2604.14152/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare contexts remains largely unknown. In this study, we conduct the first systematic audit of ASR performance on real world clinical interview data spanning Kannada, Hindi, and Indian English, comparing leading models including Indic Whisper, Whisper, Sarvam, Google speech to text, Gemma3n, Omnilingual, Vaani, and Gemini. We evaluate transcription accuracy across languages, speakers, and demographic subgroups, with a particular focus on error patterns affecting patients vs. clinicians and gender based or intersectional disparities. Our results reveal substantial variability across models and languages, with some systems performing competitively on Indian English but failing on code mixed or vernacular speech. We also uncover systematic performance gaps tied to speaker role and gender, raising concerns about equitable deployment in clinical settings. By providing a comprehensive multilingual benchmark and fairness analysis, our work highlights the need for culturally and demographically inclusive ASR development for healthcare ecosystem in India.