@inproceedings{3ee2dae301f84fa3a4a8d38dad88e7d3,
title = "Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer",
abstract = "In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.",
author = "Michael Hammond",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 20th SIGMORPHON Workshop on Computational Morphology, Phonology, and Phonetics, CMPP 2023, as part of ACL 2023 ; Conference date: 14-07-2023",
year = "2023",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "245--248",
editor = "Garrett Nicolai and Eleanor Chodroff and Cagri Coltekin and Fred Mailhot",
booktitle = "ACL 2023 - 20th SIGMORPHON Workshop on Computational Morphology, Phonology, and Phonetics, CMPP 2023",
}