Automatic speech recognition in the booth: Assessment of system performance, interpreters’ performances and interactions in the context of numbers

Bart Defrancq and Claudio Fantinuoli

Automatic Speech Recognition (ASR) has been proposed as a means to enhance state-of-the-art computer-assisted interpreting (CAI) tools and to allow machine-learning techniques to enter the workflow of professional interpreters. In this article, we test the usefulness of real-time transcription with number highlighting of a source speech for simultaneous interpreting using InterpretBank ASR. The system’s precision is high (96%) and its latency low enough to fit interpreters’ ear–voice span (EVS). We evaluate the potential benefits among first-time users of this technology by applying an error matrix and by investigating the users’ subjective perceptions through a questionnaire. The results show that the ASR provision improves overall performance for almost all number types. Interaction with the ASR support is varied and participants consult it for just over half of the stimuli. The study also provides some evidence of the psychological benefits of ASR availability and of overreliance on ASR support.

Publication history
Table of contents

This study reports on a small-scale experiment with in-booth computer-assisted interpreting (CAI). While technological support systems have been widely used in translation for decades, the first modest attempts at technological support for interpreting have only recently been made. The sheer complexity of the interpretation process – with its acoustic input and output, contextual dependence and, in the case of simultaneous interpreting, short parsing window – is one reason why technological support has, until recently, fallen short of interpreters’ expectations (Corpas Pastor and Fern 2016; Fantinuoli 2018). Given the complexity of the interpreting task, it seems clear that interpreters could benefit considerably from technological support during interpretation.

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