Interpreting from Non-Native English speakers in ESP contexts: Traditional and New Approaches

Conference interpreters are increasingly confronted with English as a Lingua Franca (ELF) or non-native English speakers and this is particularly true in conferences featuring English for Specific Purposes (ESP). Furthermore, interpreting from ELF or non-native speakers is generally more demanding in terms of cognitive load than interpreting from native English speakers (Albl-Mikasa 2010; 2013a; Albl-Mikasa et al. 2020). Since ELF and LSP seem to be linked phenomena in the conference interpreting practice, they should not be overlooked in interpreter training. Therefore, specialised speeches from non-native speakers have been introduced in students’ training corpora. However, despite considerable efforts in this direction, Simultaneous Interpreting (SI) from non-native speakers remains a major challenge for both professionals and students. From this perspective, Automatic Speech Recognition (ASR), which has recently been applied to SI (e.g. Fantinuoli 2017; Defrancq & Fantinuoli 2020), could be used to support interpreters in SI from non-native speakers. Against this background, this contribution will first present the results of a pilot study aimed at training interpreters in ELF contexts to improve their knowledge and use of ESP in SI of non-native English speakers while also contributing to exposing students to real-life situations. Secondly, the renditions of students who participated in the training will be compared with those of students who did not take part in the ELF training but who could benefit from the ASR support. The combination of specialised ELF corpus-based interpreter training with AI-based ASR could be highly beneficial for interpreting from non-native English speakers in ESP contexts.