The impact of Speech Synthesis on cognitive load and productivity during machine translation post-editing

The increased fluency of NMT output recorded in certain language pairs and domains justifies its large-scale deployment, yet professional translators are still cautious about adopting this technology. Among their main concerns is the already-documented “NMT fluency trap” that causes translators to miss significant Accuracy errors masked by the NMT output’s high fluency.
Our UniVie HAITrans research group has been investigating the potential of speech technologies – synthesis and recognition – to improve the quality of professional and trainee translators’ work. This project will specifically build on our experiments involv-ing speech synthesis in the revision and post-editing processes which show a superior level of Accuracy error detection and correction when synthesis is present. Given our findings that revising with speech synthesis does not impact negatively on the revisers’ cognitive load, we will use our eye-tracking lab to investigate cognitive load and productivity when post-editing with sound versus in silence.
Should our PEMT findings mirror our work on revision, we expect translators to feel reassured that, when integrating speech synthesis into their PEMT workflows, this tech-nology will help them avoid the NMT fluency trap.