@inproceedings{mccarthy-etal-2023-sigmorphon,
    title = "The {SIGMORPHON} 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion",
    author = "McCarthy, Arya D.  and
      Lee, Jackson L.  and
      DeLucia, Alexandra  and
      Bartley, Travis  and
      Agarwal, Milind  and
      Ashby, Lucas F.E.  and
      Del Signore, Luca  and
      Gibson, Cameron  and
      Raff, Reuben  and
      Wu, Winston",
    editor = {Nicolai, Garrett  and
      Chodroff, Eleanor  and
      Mailhot, Frederic  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.sigmorphon-1.27",
    doi = "10.18653/v1/2023.sigmorphon-1.27",
    pages = "230--238",
    abstract = "Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year{'}s task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14{\%} in the crosslingual subtask and 14{\%} in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.",
}

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year{'}s task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14{\%} in the crosslingual subtask and 14{\%} in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.
McCarthy, Arya D. Lee, Jackson L. DeLucia, Alex ra Bartley, Travis Agarwal, Milind Ashby, Lucas F.E. Del Signore, Luca Gibson, Cameron Raff, Reuben Wu, Winston
The SIGMORPHON 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion