Abstract
Pronunciation by analogy (PbA) is a data-driven method for converting letters to sound, with potential application to text-to-speech systems. We studied the capability of PbA to account for a broad range of naming latency phenomena in English. These phenomena included the lexicality, regularity, consistency, frequency, and length effects. To simulate these effects, various features of the PbA pronunciation lattice (a data structure that is produced for generating the pronunciation of a spelling pattern) were investigated. These measures included the number of arcs, nodes, pattern matchings and candidate pronunciations. While each of these individual features were able to replicate many of the effects, a measure of complexity that combined the frequency of the words as well as the number of candidates and arcs successfully simulated all of the effects tested.
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Marchand, Y., Damper, R. (2015). Simulating Naming Latency Effects. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_15
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