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Evaluating real-time voice translation in online meetings: A FAR Model analysis of EventCat performance

  • The Journal of Translation Studies
  • Abbr : JTS
  • 2025, 26(3), pp.395~423
  • DOI : 10.15749/jts.2025.26.3.013
  • Publisher : The Korean Association for Translation Studies
  • Research Area : Humanities > Interpretation and Translation Studies
  • Received : August 15, 2025
  • Accepted : September 15, 2025
  • Published : September 30, 2025

Munjung Bae 1

1영남대학교

Accredited

ABSTRACT

This study investigates the quality of real-time voice translation without post-editing in an online meeting context, focusing on the AI interpretation engine, EventCat. While recent advancements in neural machine translation have led to widespread expectations of near-perfect automated interpretation, few empirical studies have examined its performance in real-time spoken scenarios. Unlike most prior research that primarily analyzed post-edited content, this study evaluated translation quality in a live-like setting by inviting the AI interpreter to a Zoom meeting and streaming actual speeches from formal events as input. Translation quality was evaluated through Pedersen’s (2017) FAR model, a framework that measures functional equivalence, acceptability, and readability. Results indicate that unedited machine translation (MT) output still does not meet the quality standards required in high-stakes situations, particularly in maintaining semantic coherence and contextual fidelity. This research contributes to the growing body of literature on real-time MT evaluation by extending the scope to spontaneous speech in virtual conferencing environments and providing empirical evidence on the limitations and potential of AI interpretation tools in professional settings.

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