Automatic Speech Recognition in Consecutive Interpreter Workstation: Computer-Aided Interpreting Tool ‘Sight-Terp’

Abstract

This experimental study investigates the effect of using an automatic speech recognition (ASR)-enhanced computer-assisted interpreting (CAI) tool “Sight-Terp” on the performances of a group of participants in consecutive interpreting tasks. Sight-Terp, which is designed and developed by the author of this study, provides a digital note-pad, real-time speech translation, named entity recognition and highlighting, and automatic segmentation of a speech. The study employs a within-subjects repeated measures design to test participants' performances in two conditions (with and without Sight-Terp) and analyses their performances based on the criteria of accuracy and fluency. In seeking the significant difference between the accuracy ratios in two different conditions, accuracy was measured by the average of the number of accurately conveyed units of meaning (Seleskovitch, 1989). Fluency, on the other hand, was measured by calculating the total number of occurrences of disfluency markers such as false starts, frequency of filled pauses, filler words, whole-word repetitions, broken words, and incomplete phrases for each performance. Additionally, a follow-up qualitative survey is conducted to obtain participants' comparative responses and perceptions of the tool usage. The analysis and quantitative results of the study indicate that leveraging the ASR-integrated CAI tool Sight-Terp led to an enhancement in the accuracy of the participants' interpretations. However, this also resulted in a higher occurrence of disfluencies and elongated durations of interpretations. While the users experienced little difficulty while using the tool, the study outcomes also suggest potential areas of improvement and modifications that could further enhance the utility of the tool. The study aims to inform interpreting education and practice by highlighting the benefits and challenges of incorporating ASR technology in the interpreting process and offers practical suggestions for the future development of CAI tools for interpreters.