Year: 2026
Type: conference-paper
Abstract: This study compares the syllable structures of nine Arabic varieties from Wiktionary, using a computational syllabifier.It further investigates methods for learning syllable boundaries in unsyllabified words transcribed in the International Phonetic Alphabet (IPA).The syllabification algorithm is evaluated under three conditions: (i) Default, employing fixed rules; (ii) Joint, learning onsets and codas across all varieties collectively; and (iii) Per-variety, learning onsets and codas specific to each variety.Results indicate that the default configuration yields the highest accuracy, ranging from 97.05% to 100%.The per-variety approach achieves 90.64% to 100% accuracy, while the joint approach ranges from 84.63% to 94.74%.A cross-variety analysis using Jensen-Shannon divergence reveals three principal groupings: Egyptian, Hejazi, and Modern Standard Arabic are closely related; Levantine and Gulf varieties constitute a second cluster; and Juba Arabic, Maltese, and Moroccan emerge as outliers.A cleaned dataset encompassing all nine varieties is also provided.
Institution: Stony Brook University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Stony Brook University
OpenAlex ID: W7140084480
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