  We present enhancements to a speech-to-speech translation pipeline in order
to perform automatic dubbing. Our architecture features neural machine
translation generating output of preferred length, prosodic alignment of the
translation with the original speech segments, neural text-to-speech with fine
tuning of the duration of each utterance, and, finally, audio rendering to
enriches text-to-speech output with background noise and reverberation
extracted from the original audio. We report on a subjective evaluation of
automatic dubbing of excerpts of TED Talks from English into Italian, which
measures the perceived naturalness of automatic dubbing and the relative
importance of each proposed enhancement.
