  We investigate how humans perform the task of dubbing video content from one
language into another, leveraging a novel corpus of 319.57 hours of video from
54 professionally produced titles. This is the first such large-scale study we
are aware of. The results challenge a number of assumptions commonly made in
both qualitative literature on human dubbing and machine-learning literature on
automatic dubbing, arguing for the importance of vocal naturalness and
translation quality over commonly emphasized isometric (character length) and
lip-sync constraints, and for a more qualified view of the importance of
isochronic (timing) constraints. We also find substantial influence of the
source-side audio on human dubs through channels other than the words of the
translation, pointing to the need for research on ways to preserve speech
characteristics, as well as semantic transfer such as emphasis/emotion, in
automatic dubbing systems.
