In 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. Index Terms: automatic syllabification, rule-based techniques, artificial neural networks, text-to-speech Beck, J., Braga, D., Nogueira, J., Coelho, L. and Dias, M. (2009). “Automatic Syllabification for Danish Text-to-Speech Systems”, in Proceedings of Interspeech 2009, September 6-10, Brighton, United Kingdom. 2009. https://www.isca-speech.org/archive/archive_papers/interspeech_2009/papers/i09_1287.pdf https://www.isca-speech.org/archive/interspeech_2009/index.html

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automatic syllabification,rule-based techniques,artificial neural networks,text-to-speech

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