ASR提升远程医疗翻译质量,全字幕和摘要更有效
Integrating automatic speech recognition into remote healthcare interpreting: A pilot study of its impact on interpreting quality
- 用脚本模拟对话翻译,对比四种ASR输出方式
- 全字幕和ChatGPT摘要显著降低翻译错误率
- 适合医疗翻译研究者与人机协同系统开发者
本论文报告了一项试点研究的结果,探讨自动语音识别(ASR)技术对远程医疗翻译中翻译质量的影响。研究采用被试内实验设计,设置四种随机条件,使用脚本化医疗咨询模拟对话翻译任务,涉及四位中英双语训练译员。通过提示回溯报告和半结构化访谈收集参与者对ASR支持的体验与感知。初步数据显示,提供完整ASR字幕及基于ASR生成的ChatGPT摘要,能有效提升翻译质量。不同类型的ASR输出对错误类型分布产生不同影响。参与者对技术交互体验相似,普遍偏好完整字幕。该试点研究显示将ASR应用于对话式医疗翻译具有积极前景,并为优化ASR输出呈现方式提供了见解。但需强调,本研究主要目的为验证方法论,未来需更大样本量研究以确认结果。
原文摘要 · Abstract (English)
This paper reports on the results from a pilot study investigating the impact of automatic speech recognition (ASR) technology on interpreting quality in remote healthcare interpreting settings. Employing a within-subjects experiment design with four randomised conditions, this study utilises scripted medical consultations to simulate dialogue interpreting tasks. It involves four trainee interpreters with a language combination of Chinese and English. It also gathers participants' experience and perceptions of ASR support through cued retrospective reports and semi-structured interviews. Preliminary data suggest that the availability of ASR, specifically the access to full ASR transcripts and to ChatGPT-generated summaries based on ASR, effectively improved interpreting quality. Varying types of ASR output had different impacts on the distribution of interpreting error types. Participants reported similar interactive experiences with the technology, expressing their preference for full ASR transcripts. This pilot study shows encouraging results of applying ASR to dialogue-based healthcare interpreting and offers insights into the optimal ways to present ASR output to enhance interpreter experience and performance. However, it should be emphasised that the main purpose of this study was to validate the methodology and that further research with a larger sample size is necessary to confirm these findings.
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