Wangyi Tang
About
My research project
Disrupter or enabler? Assessing the impact of using automatic speech recognition technology in interpreter-mediated legal proceedingsAs part of the professional re-design in response to emerging interpreting technologies and AI advancements, my project aims to evaluate the influence of automatic speech recognition (ASR)-generated AI on remote interpreters in legal settings. The project consists of two stages:
The first stage involves developing a quality assessment method that takes into account the use of ASR technology.
The second stage focuses on evaluating the performance of remote legal interpreters under both ASR and non-ASR conditions using the system developed in the first stage. The goal is to observe how interpreters adapt to the presence of ASR technology.
Supervisors
As part of the professional re-design in response to emerging interpreting technologies and AI advancements, my project aims to evaluate the influence of automatic speech recognition (ASR)-generated AI on remote interpreters in legal settings. The project consists of two stages:
The first stage involves developing a quality assessment method that takes into account the use of ASR technology.
The second stage focuses on evaluating the performance of remote legal interpreters under both ASR and non-ASR conditions using the system developed in the first stage. The goal is to observe how interpreters adapt to the presence of ASR technology.
My qualifications
ResearchResearch interests
Interpreting, Human Computer Interaction
Research interests
Interpreting, Human Computer Interaction
Publications
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.