Machine translation (MT) has come a long way and is readily employed in production systems to serve millions of users daily. With the recent advances in generative AI, a new form of translation is becoming possible - video dubbing. This work motivates the importance of isochronic translation, especially in the context of automatic dubbing, and introduces `IsoChronoMeter' (ICM). ICM is a simple yet effective metric to measure isochrony of translations in a scalable and resource-efficient way without the need for gold data, based on state-of-the-art text-to-speech (TTS) duration predictors. We motivate IsoChronoMeter and demonstrate its effectiveness. Using ICM we demonstrate the shortcomings of state-of-the-art translation systems and show the need for new methods. We release the code at this URL: \url{https://github.com/braskai/isochronometer}.
@article{arxiv.2410.11127,
title = {IsoChronoMeter: A simple and effective isochronic translation evaluation metric},
author = {Nikolai Rozanov and Vikentiy Pankov and Dmitrii Mukhutdinov and Dima Vypirailenko},
journal= {arXiv preprint arXiv:2410.11127},
year = {2024}
}
Comments
WMT24 (co-located with EMNLP24), Accepted to Shared Task Track, 6 pages, 2 figures, 4 tables, 2 pages references