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Automatic syllabification for danish text-to-speech systems
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Automatic syllabification Rule-based techniques Artificial neural networks Text-to-speech
Contexto Educativo
Citação
Editora
International Speech Communication Association