The Impact Of Using Bionic Font In Speech-To-Text Tools On The Accuracy Of English-Into-Arabic Interpretation

Consecutive interpreting places considerable cognitive demands on interpreters, who must simultaneously manage listening, note-taking, comprehension, and verbal output under time pressure. This study aims to examine the potential of Bionic Reading font—a typographic system that highlights the initial parts of words to enhance visual fixation—to reduce cognitive load and improve the accuracy of English-into-Arabic consecutive interpretation. It also seeks to determine whether integrating this font into Automatic Speech Recognition (ASR) tools can enhance interpreters’ real-time performance by facilitating faster comprehension and minimizing loss of meaning. A quasi-experimental within-subject design was employed. Five professional Arabic-speaking interpreters performed consecutive interpretation tasks under two distinct conditions: one using ASR transcripts displayed in a standard font, and another using transcripts formatted in Bionic Reading font. Interpretation output was analyzed using Daniel Gile’s EOI framework, which identifies and categorizes Errors, Omissions, and Infelicities. The findings suggest that the use of Bionic Reading font within ASR tools contributes meaningfully to improved interpretation accuracy, particularly by reducing cognitive strain and enhancing lexical recognition. These results support the development of interpreter-friendly ASR tools with customizable visual settings, affirming the role of visual text design in promoting cognitive efficiency and linguistic precision. The study opens new pathways for research into multimodal interpreter support and accessible technology design.