In a four-participant pilot, full ASR transcripts and ASR-fed ChatGPT summaries significantly improved trainee interpreters' quality scores in simulated remote healthcare consultations, but partial ASR support did not.
Defining maximum acceptable latency of AI-enhanced CAI tools
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abstract
Recent years have seen an increasing number of studies around the design of computer-assisted interpreting tools with integrated automatic speech processing and their use by trainees and professional interpreters. This paper discusses the role of system latency of such tools and presents the results of an experiment designed to investigate the maximum system latency that is cognitively acceptable for interpreters working in the simultaneous modality. The results show that interpreters can cope with a system latency of 3 seconds without any major impact in the rendition of the original text, both in terms of accuracy and fluency. This value is above the typical latency of available AI-based CAI tools and paves the way to experiment with larger context-based language models and higher latencies.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Integrating automatic speech recognition into remote healthcare interpreting: A pilot study of its impact on interpreting quality
In a four-participant pilot, full ASR transcripts and ASR-fed ChatGPT summaries significantly improved trainee interpreters' quality scores in simulated remote healthcare consultations, but partial ASR support did not.