Automatic speech recognition in the booth
Bart Defrancq, Claudio Fantinuoli
Abstract
Abstract 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.