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

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.