https://orcid.org/0000-0002-5148-0273
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Abstract
This paper examines links between perfect rhymes and text readability and decoding using a measure of English rhymes called the Perfect Rhymes Dictionary (PeRDict). PeRDict is based on the Carnegie Mellon University Pronouncing Dictionary (the CMUdict) and provides rhyme counts for ∼48,000 words in English and for the most frequent 1,000, 2,500, 5,000, and 10,000 rhymes within the dictionary as measured by the Corpus of American English (COCA). Two assessments of PeRDICT are presented. The first examines the strength of rhyme features to predict text readability in conjunction with word neighborhood density effects reported by the English Lexicon Project (ELP) and a word frequency measure. The second examines the strength of rhyme features to predict word decoding in conjunction with word neighborhood effects and a word frequency measure. In both assessments, the number of rhymes per word was predictive of text or word processing beyond features related to word neighborhood effects and word frequency. Word rhyme counts performed more strongly in predicting text processing versus word processing.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The PeRDict database is available at https://github.com/scrosseye/PeRDict-database. All data and materials for all experiments reported in this paper are available at https://github.com/scrosseye/PeRDict-Analyses.
Notes
1 The PeRDict database is freely available at https://github.com/scrosseye/PeRDict-database.
2 Studies have indicated that awareness of smaller units of sound at the phoneme level may be better predictors of differences in reading proficiency in English than larger units of sound like rhymes (Hoien, Lundberg, Stanovich, & Bjaalid, Citation1995; Muter, et al., Citation1998; Nation & Hulme, Citation1997).
3 See Yarkoni et al. (Citation2008) for counterevidence to this general finding.
4 The website that hosted the dataset reported in De Cara and Goswami (Citation2002) is no longer active.
5 The Python script used for this task is available at https://github.com/scrosseye/PeRDict-Analyses.
6 All R scripts and data for the analyses in this paper are available at https://github.com/scrosseye/PeRDict-Analyses.