ARTIFICIAL INTELLIGENCE IN SIMULTANEOUS INTERPRETING TRAINING: AN EXPERIMENTAL STUDY OF SPEECH-TO-TEXT TECHNOLOGY

Abstract

As the world is rushing relentlessly to incorporate technology and artificial intelligence into various walks of life under allegations like 'development', researchers should investigate the potential impacts of such a movement from different perspectives. Simultaneous interpreting (SI) is no exception per se. The present study aimed to explore how useful the technological advances achieved in artificial intelligence can be in SI training (process and performance) through an experimental study of a speech-to-text technology. It adopted both qualitative and quantitative methodological approaches using analysis, comparison, assessment, questionnaire and experiment as research tools. In this human-machine interaction, sample original English speeches (in Language B) were interpreted simultaneously into Arabic (Language A) by participants/trainees representing fourth year university students, with the help of a speech-to-text model. The significance of the study lies mainly in its potential implications for the industry, training and education, and research. It found out that STT in its current form is a failure and that the suggested model proved some success although the results were quite modest.