@inproceedings{rozanov-etal-2024-isochronometer,
    title = "{I}so{C}hrono{M}eter: A Simple and Effective Isochronic Translation Evaluation Metric",
    author = "Rozanov, Nikolai  and
      Pankov, Vikentiy  and
      Mukhutdinov, Dmitrii  and
      Vypirailenko, Dima",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.wmt-1.29/",
    doi = "10.18653/v1/2024.wmt-1.29",
    pages = "372--379",
    abstract = "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 {\textquoteleft}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}."
}