Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Machine Translation Verbosity Control for Automatic Dubbing (2110.03847v1)

Published 8 Oct 2021 in cs.CL, cs.SD, and eess.AS

Abstract: Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language. The task implies many challenges, one of which is generating translations that not only convey the original content, but also match the duration of the corresponding utterances. In this paper, we focus on the problem of controlling the verbosity of machine translation output, so that subsequent steps of our automatic dubbing pipeline can generate dubs of better quality. We propose new methods to control the verbosity of MT output and compare them against the state of the art with both intrinsic and extrinsic evaluations. For our experiments we use a public data set to dub English speeches into French, Italian, German and Spanish. Finally, we report extensive subjective tests that measure the impact of MT verbosity control on the final quality of dubbed video clips.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Surafel M. Lakew (12 papers)
  2. Marcello Federico (38 papers)
  3. Yue Wang (677 papers)
  4. Cuong Hoang (4 papers)
  5. Yogesh Virkar (9 papers)
  6. Roberto Barra-Chicote (24 papers)
  7. Robert Enyedi (5 papers)
Citations (21)

Summary

We haven't generated a summary for this paper yet.