Beck, JeppeBraga, DanielaNogueira, JoãoSales-Dias, MiguelCoelho, Luís2016-02-052016-02-052009978-1-61567-692-7http://hdl.handle.net/10400.22/7635In this paper, a rule-based automatic syllabifier for Danish is described using the Maximal Onset Principle. Prior success rates of rule-based methods applied to Portuguese and Catalan syllabification modules were on the basis of this work. The system was implemented and tested using a very small set of rules. The results gave rise to 96.9% and 98.7% of word accuracy rate, contrary to our initial expectations, being Danish a language with a complex syllabic structure and thus difficult to be rule-driven. Comparison with data-driven syllabification system using artificial neural networks showed a higher accuracy rate of the former system.engAutomatic syllabificationRule-based techniquesArtificial neural networksText-to-speechconference object