Abstract
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.
Abstract (translated)
这篇论文报道了一项初步研究的结果，该研究探讨了自动语音识别（ASR）技术对远程医疗口译服务质量的影响。采用一种包含四种随机条件的被试内实验设计，本研究利用编写的医学咨询对话来模拟口译任务，并涉及四位中英语言组合的实习口译员。此外，还通过提示式回顾报告和半结构化访谈收集参与者对ASR支持的经验与看法。 初步数据表明，ASR（特别是完整ASR转录文本和基于ASR由ChatGPT生成的摘要）的可用性显著提升了口译质量。不同类型ASR输出对口译错误类型的分布产生了不同的影响。参与者报告了相似的技术互动体验，并表示他们更倾向于使用完整的ASR转录文本。这项初步研究展示了将ASR应用于对话式医疗口译中的有希望的结果，为优化呈现ASR输出以增强口译员经验和表现提供了见解。 然而，需要强调的是，本研究的主要目的是验证方法学的有效性，因此还需要进行具有更大样本量的研究来确认这些发现。
URL
https://arxiv.org/abs/2502.03381
