Machine and Computer-assisted Interpreting: Innovations in and Implications for Interpreting Practice, Pedagogy and Research

Abstract

Transformative advancements in technologies such as Automatic Speech Recognition, Natural Language Processing, Deep Learning, and Generative AI have significantly accelerated the evolution of both Machine Interpreting (MI) and Computer-Assisted Interpreting (CAI), fundamentally reshaping the interpreting ecosystem. MI has progressed from being based on statistical machine-translation (MT) models to being augmented by large-language and multimodal models, while undergoing a transition from cascade to end-to-end systems. These developments have markedly enhanced MI’s capacity to manage increasingly complex and diverse domains, linguistic features, and operational contexts. CAI, originally designed to streamline the preparatory processes for interpreting, has evolved to incorporate real-time functionalities that are integrated into the interpreting workflow, enabling the effective management of complex terminology and other problem-triggers. This introduction begins by providing a comprehensive overview of the evolution, current state, and future directions of MI and CAI, followed by an introduction to the seven contributions featured in this special issue. These studies encompass a diverse range of topics, including MI quality evaluation, a comparison of human–machine interpreting products and process, CAI tool assessment in remote simultaneous interpreting (SI), the implications of automatic speech recognition (ASR) for consecutive interpreting, the effect of multimodal inputs, and the user–machine interaction patterns with live captioning in SI. This introduction, along with the seven contributions in this issue, aims to advance the growing body of knowledge on the transformative impact of MI and CAI in reshaping interpreting practice, pedagogy, and research.