针对希腊语医疗录音开发了专用语音识别系统，提升病历转录准确率。
Automatic Speech Recognition for Greek Medical Dictation
- 结合声学与文本模型，适配希腊语医疗术语和语言变体。
- 通过领域微调，实现更准确连贯的医疗文本转录。
- 适合需要自动化病历录入的医疗机构使用。
医疗语音转录系统是现代医疗中不可或缺的工具，可高效准确地将语音转化为书面病历文档。本文旨在构建一个面向希腊语医疗场景的专用语音识别系统，目标是帮助医护人员减轻手动记录负担，提升工作效率。为此，我们设计了一种融合自动语音识别与文本纠错模型的系统，以更好应对希腊语医疗领域的专业术语和语言多样性。该方法结合声学建模与文本建模，提升转录的真实性和可靠性。重点在于对现有语音与语言技术进行希腊语医疗场景的适配，解决复杂医学术语及语言不一致等挑战。通过领域特定的微调，系统实现了更高准确率和更强连贯性的转录结果，推动了希腊语医疗语言技术的实际应用。
原文摘要 · Abstract (English)
Medical dictation systems are essential tools in modern healthcare, enabling accurate and efficient conversion of speech into written medical documentation. The main objective of this paper is to create a domain-specific system for Greek medical speech transcriptions. The ultimate goal is to assist healthcare professionals by reducing the overload of manual documentation and improving workflow efficiency. Towards this goal, we develop a system that combines automatic speech recognition techniques with text correction model, allowing better handling of domain-specific terminology and linguistic variations in Greek. Our approach leverages both acoustic and textual modeling to create more realistic and reliable transcriptions. We focused on adapting existing language and speech technologies to the Greek medical context, addressing challenges such as complex medical terminology and linguistic inconsistencies. Through domain-specific fine-tuning, our system achieves more accurate and coherent transcriptions, contributing to the development of practical language technologies for the Greek healthcare sector.
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