Automatic speech synthesis has seen rapid development and integration in domains as diverse as accessibility services, translation, or language learning platforms. We analyse its integration in a post-editing machine translation (PEMT) environment and the effect this has on quality, productivity, and cognitive effort. We use Bayesian hierarchical modelling to analyse eye-tracking, time-tracking, and error annotation data resulting from an experiment involving 21 professional translators post-editing from English into German in a customised cloud-based CAT environment and listening to the source and/or target texts via speech synthesis. Using speech synthesis in a PEMT task has a non-substantial positive effect on quality, a substantial negative effect on productivity, and a substantial negative effect on the cognitive effort expended on the target text, signifying that participants need to allocate less cognitive effort to the target text.

This EAMT-funded eye-tracking study investigates the impact of Machine Translation Post-Editing and Automatic Speech Recognition on English-Romanian translations of patient-facing medical texts. This paper provides an overview of the study objectives, setup and preliminary results.