Resumen de Assessing the impact of automatic speech recognition on remote simultaneous interpreting performance using the NTR Model

Eloy Rodríguez González, Muhammad Ahmed Saeed, Tomasz Korybski, Elena Davitti, Sabine Braun

• The emergence of Simultaneous Interpreting Delivery Platforms (SIDPs) has opened up new opportunities for interpreters to provide cloud-based remote simultaneous interpreting (RSI) services. Similar to booth-based RSI, which has been shown to be more tiring than conventional simultaneous interpreting and more demanding in terms of information processing and mental modelling [11; 12], cloud-based RSI configurations are perceived as more stressful than conventional simultaneous interpreting and potentially detrimental to interpreting quality [2]. Computer-assisted interpreting (CAI) tools, including automatic speech recognition (ASR) [8], have been advocated as a means to support interpreters during cloud-based RSI assignments, but their effectiveness is underexplored.The study reported in this article experimentally investigated the impact of providing interpreters with access to an ASR-generated live transcript of the source speech while they were interpreting, examining its effect on their performance and overall user experience. As part of the experimental design, 16 professional conference interpreters performed a controlled interpreting test consisting of a warmup speech (not included in the analysis), and four speeches, i.e., two lexically dense speeches and two fast speeches, presented in two different interpreting conditions, i.e., with and without ASR support. This article presents initial quantitative findings from the analysis of the interpreters’ performance, which was conducted using the NTR Model [17]. Overall, the findings reveal a reduction in the total number of interpreting errors in the ASR condition. However, this is accompanied by a loss in stylistic quality in the ASR condition.