Does that sound right? A novel method of evaluating models of reading aloud: Rating nonword pronunciations

Michele Gubian, Ryan Blything*, Colin J Davis, Jeffrey S Bowers

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)
39 Downloads (Pure)

Abstract

Nonword pronunciation is a critical challenge for models of reading aloud but little attention has been given to identifying the best method for assessing model predictions. The most typical approach involves comparing the model’s pronunciations of nonwords to pronunciations of the same nonwords by human participants and deeming the model’s output correct if it matches with any transcription of the human pronunciations. The present paper introduces a new ratings-based method, in which participants are shown printed nonwords and asked to rate the plausibility of the provided pronunciations, generated here by a speech synthesiser. We demonstrate this method with reference to a previously published database of 915 disyllabic nonwords (Mousikou et al., 2017). We evaluated two well-known psychological models, RC00 and CDP++, as well as an additional grapheme-to-phoneme algorithm known as Sequitur, and compared our model assessment with the corpus-based method adopted by Mousikou et al. We find that the ratings method: a) is much easier to implement than a corpus-based method, b) has a high hit rate and low false-alarm rate in assessing nonword reading accuracy, and c) provided a similar outcome as the corpus-based method in its assessment of RC00 and CDP++. However, the two methods differed in their evaluation of Sequitur, which performed much better under the ratings method. Indeed, our evaluation of Sequitur revealed that the corpus-based method introduced a number of false positives and more often, false negatives. Implications of these findings are discussed.
Original languageEnglish
JournalBehavior Research Methods
DOIs
Publication statusPublished - 1 Jun 2022

Bibliographical note

Funding Information:
Funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 741134).

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Reading aloud
  • Computational reading models
  • Generalization
  • Pronunciation

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