Abstract
The task of finding syllable boundaries can be straightforward or challenging, depending on the language. Text-to-speech applications have been shown to perform considerably better when syllabication, whether orthographic or phonetic, is employed as a means of breaking down the text into units bellow word level. Romanian syllabication is non-trivial mainly but not exclusively due to its hiatus-diphthong ambiguity. This phenomenon affects both phonetic and orthographic syllabication. In this paper, we focus on orthographic syllabication for Romanian and show that the task can be carried out with a high degree of accuracy by using sequence tagging. We compare this approach to support vector machines and rule-based methods. The features we used are simply character n-grams with end-of-word marking.
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Dinu, L.P., Niculae, V., Sulea, OM. (2013). Romanian Syllabication Using Machine Learning. In: Habernal, I., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2013. Lecture Notes in Computer Science(), vol 8082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40585-3_57
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DOI: https://doi.org/10.1007/978-3-642-40585-3_57
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