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Abstract
Phonological distance can be measured computationally using formally specified algorithms. This work investigates two such measures, one developed by Nerbonne and Heeringa (Citation1997) based on Levenshtein distance (Levenshtein, Citation1965) and the other an adaptation of Dunning's (Citation1994) language classifier that uses maximum likelihood distance. These two measures are compared against naïve transcriptions of the speech of paediatric cochlear implant users. The new measure, maximum likelihood distance, correlates highly with Levenshtein distance and naïve transcriptions; results from this corpus are easier to obtain since cochlear implant speech has a lower intelligibility than the usually high intelligibility of the speech of a different dialect.
Acknowledgements
The project described was supported by grant R01DC005594 from the National Institutes of Health to Indiana University. We would also like to thank Cara Lento Kaiser and Amy P. Teoh for assistance with phonetic transcriptions, and Andrew K. Kirk and Jason K. An for assistance with naïve listener transcriptions.