面向低资源医疗领域会话电话语音的混合ASR系统开发
Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech
AI总结：
本文针对德语-阿拉伯语和德语-越南语医疗会话电话语音翻译任务，开发混合ASR系统，通过数据收集、多种声学模型架构设计及方言困难处理，支持非专业口译员以改善医患沟通。
摘要：
语言障碍在我们日益互联和全球化的世界中构成了巨大挑战。尤其是在医疗领域，例如医院或急诊室，沟通困难和延误可能导致医疗事故和非最优的患者护理。在HYKIST项目中，我们关注医患沟通，具体来说是讲德语的医生与讲阿拉伯语或越南语的患者之间的沟通。目前，医生可以致电Triaphon服务，获得口译员的帮助以促进沟通。HYKIST的目标是通过自动语音翻译系统支持通常非专业的双语口译员，以改善患者护理并帮助克服语言障碍。在这项工作中，我们介绍了针对该医疗领域会话电话语音翻译任务的ASR系统开发工作，涉及两个语言对、数据收集、多种声学模型架构以及方言引起的困难。
英文摘要：
Language barriers present a great challenge in our increasingly connected and global world. Especially within the medical domain, e.g. hospital or emergency room, communication difficulties and delays may lead to malpractice and non-optimal patient care. In the HYKIST project, we consider patient-physician communication, more specifically between a German-speaking physician and an Arabic- or Vietnamese-speaking patient. Currently, a doctor can call the Triaphon service to get assistance from an interpreter in order to help facilitate communication. The HYKIST goal is to support the usually non-professional bilingual interpreter with an automatic speech translation system to improve patient care and help overcome language barriers. In this work, we present our ASR system development efforts for this conversational telephone speech translation task in the medical domain for two languages pairs, data collection, various acoustic model architectures and dialect-induced difficulties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
