# DASHA: Diarization-Aware Speech-LLM ASR for Healthcare in Code-Switched Indic Doctor-Patient Conversations

arXiv: https://arxiv.org/abs/2603.06373 · HTML: https://mlsanity.com/paper/2603.06373/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [SamaVaani: Auditing and Debiasing Multilingual Clinical ASR for Indian Languages](https://mlsanity.com/paper/2606.26901/) (2026)

## Abstract (arXiv)

Extracting patients' medical conditions from recorded primary-care conversations requires knowing who said what, which is difficult when speech is far-field, spontaneous, overlapping and code-switched. We study this problem on DISPLACE-M, a corpus of Hindi--English (Hinglish) doctor--patient conversations, and propose DASHA, a modular cascade of diarization, speaker-attributed ASR (SA-ASR) and LLM-based extraction. An end-to-end neural diarization with vector clustering (EEND-VC) system built on a multilingual w2v-BERT~2.0 encoder reduces DER from 9.31% to 7.76%. A Qwen3-ASR model adapted to Hindi and the target domain, with Devanagari normalization, reduces tcpWER from 26.78% to 19.27%, and to 18.59% with optional LLM correction. A module-swap analysis shows that better diarization improves extraction only when paired with the adapted ASR. On code-switched words, the multilingual encoder roughly halves speaker confusion. DASHA sets a new state of the art for two-speaker, code-switched Hindi SA-ASR and ranked first among 25 participants in the DISPLACE-M challenge.