Translation Dictation vs. Post-editing with Cloud-based Voice Recognition: A Pilot Experiment
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In this paper, we report on a pilot mixed-methods experiment investigating the effects on
productivity and on the translator experience of integrating machine translation (MT) postediting (PE) with voice recognition (VR) and translation dictation (TD). The experiment
was performed with a sample of native Spanish participants. In the quantitative phase of the
experiment, they performed four tasks under four different conditions, namely (1)
conventional TD; (2) PE in dictation mode; (3) TD with VR; and (4) PE with VR (PEVR).
In the follow-on qualitative phase, the participants filled out an online survey, providing
details of their perceptions of the task and of PEVR in general. Our results suggest that
PEVR may be a usable way to add MT to a translation workflow, with some caveats. When
asked about their experience with the tasks, our participants preferred translation without the
‘constraint’ of MT, though the quantitative results show that PE tasks were generally more
efficient. This paper provides a brief overview of past work exploring VR for from-scratch
translation and PE purposes, describes our pilot experiment in detail, presents an overview
and analysis of the data collected, and outlines avenues for future work.