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New model DiaWhisper-DPO improves clinical interview transcription and role attribution

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DiaWhisper-DPO, an end-to-end model for transcribing clinical interviews and attributing utterances to either the clinician or patient. This model fine-tunes Whisper-large-v3 using LoRA and an auxiliary role head, and further refines performance with DiaWhisper-DPO, which leverages decoding failures as negative examples for preference optimization without requiring human annotation. The system demonstrated significant improvements on the DAIC-WoZ dataset, achieving 0.973 role accuracy and a 72% reduction in DER compared to cascaded baselines, while also showing strong performance on the cross-lingual PDCH-HAMD dataset. AI

IMPACT Enhances accuracy in clinical dialogue analysis, potentially improving automated depression screening tools.

RANK_REASON Research paper detailing a new model for clinical interview transcription. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New model DiaWhisper-DPO improves clinical interview transcription and role attribution

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Research paper detailing a new model for clinical interview transcription. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Weiming Li, Ana Catarina Fidalgo Barata, Miguel Constante, Jo\~ao Miguel Sanches ·

    DiaWhisper-DPO: Role-Attributed Transcription of Clinical Interviews via Failure-Mined Preference Optimization

    arXiv:2609.16661v1 Announce Type: new Abstract: Automated depression screening from clinical interviews requires attribution of utterances to the clinician or patient. We evaluate two datasets: DAIC-WOZ, where participant-only recordings require re-synthesizing both sides for con…

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