The impact of using bionic font in speech-to-text Tools on the accuracy of English into Arabic interpretation /

Abstract

Consecutive interpreting (CI) requires significant cognitive effort, with interpreters juggling listening, note-taking, and verbal output in real time. This study investigates whether the Bionic Reading font, designed to enhance reading efficiency, could reduce cognitive load and improve accuracy in Arabic CI tasks. It aims to determine whether the Bionic Reading font could optimize the user experience of Speech-to-Text (STT) tools, leading to reduced cognitive load and improved CI accuracy. A quasi-experimental design is used, with participants (n=5) engaging in an interpreting task under both font conditions. Pre- and post-test questionnaires evaluates readability, processing speed, comprehension, comfort, interpreting accuracy, cognitive load, and font preference. Performance is further analyzed using Daniel Gile’s Effort Model, which provided a framework for assessing cognitive strain, and through Errors, Omissions, and Infelicities (EOI) analysis, which offers a systematic means of evaluating interpreting quality. The findings infoms the development of user-friendly STT tools with customizable font options, offering practical implications for enhancing communication accessibility and effectiveness, and laying the groundwork for further exploration of this technology’s potential across languages.