Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis
Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use cases. Although large-scale pretrained ASR models such as Whisper achieve strong generalisation, their behaviour after medical and multilingual adaptation remains insufficiently understood beyond word error rate (WER). This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis. We compare zero-shot decoding, English-only fine-tuning, German-only diagnostic
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- LinkedLinked via arxiv author · 85%Souranil Kahali →
“Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis”
- LinkedLinked via arxiv author · 85%Rituparna Bose →
“Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis”
- LinkedLinked via arxiv author · 85%Abner Hernandez →
“Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis”
- LinkedLinked via arxiv author · 85%Tomas Arias-Vergara →
“Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis”