# Zero-Shot End-To-End Spoken Question Answering In Medical Domain Source: [Zero-Shot End-To-End Spoken Question Answering In Medical Domain]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:51:14.866Z Authors: Yanis Labrak, Adel Moumen, Richard Dufour, Mickael Rouvier Published: 2024-06-09T18:13:36.000Z ## Abstract In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development. Conventional approaches often entail the use of separate models for question audio transcription and answer selection, resulting in significant resource utilization and error accumulation. To tackle these challenges, we explore the effectiveness of end-to-end (E2E) methodologies for SQA in the medical domain. Our study introduces a novel zero-shot SQA approach, compared to traditional cascade systems. Through a comprehensive evaluation conducted on a new open benchmark of 8 medical tasks and 48 hours of synthetic audio, we demonstrate that our approach requires up to 14.7 times fewer resources than a combined 1.3B parameters LLM with a 1.55B parameters ASR model while improving average accuracy by 0.5\\%. These findings underscore the potential of E2E methodologies for SQA in resource-constrained contexts. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()