Shiyi Tan

Research on Automatic Speech Recognition (ASR)-assisted interpreting has concentrated on simultaneous conference interpreting, leaving remote public service settings underexplored. This article reviews methodological approaches to ASR-assisted interpreting research and presents two pilot studies on remote legal and medical interpreting. In the legal pilot, two professional interpreters completed ASR and non-ASR tasks in simultaneous and consecutive modes; ASR was associated with higher NTR accuracy and lower weighted error scores, especially in consecutive tasks, while interviews showed selective transcript use alongside style- and attention-related trade-offs. In the medical pilot, four trainee interpreters completed four ASR display conditions; full transcripts and ASR-fed summaries were associated with lower perceived workload, while the eye-tracking component exposed design issues requiring refinement. Together, the pilots show how domain-specific conditions shape research design, user responses, and the methodological challenges of studying AI-assisted interpreting in Public Service Interpreting.

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.