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Learning english syllabification rules
1998This paper describes LE-SR (Learning English Syllabification Rules), the first machine learning program that learns English syllabification rules, i.e., rules that tell how to divide English words into syllables for pronunciation. LE-SR uses a unique knowledge representation called C-S-CL-SS which effectively generalizes English graphemes.
Jian Zhang, Howard J. Hamilton
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A connectionist account of French syllabification
Lingua, 1995Abstract In this paper, we present a connectionist approach to phonology. We show that a process like syllabification, which in a symbolic framework is generally thought of as being the outcome of some kind of parsing by a dedicated algorithm, can be accounted for in a pure dynamic way.
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Flipping onsets to enhance syllabification
International Journal of Speech Technology, 2019Two-year-old children who start learning to speak generally spell a polysyllabic word by flipping onsets of consecutive syllables. Sometimes they speak unclearly, hard to understand since the flipped onsets produce another word that has a much different meaning.
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Morphology and syllabification domains
Lingua, 1995Abstract The morphological level that serves as a domain for syllabification rules is commonly seen as a parameter along which languages differ. An examination of syllabification at different levels of affixation in English, German, Dutch, and French narrows the relevant levels down to two, the word and the ‘morpheme’.
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Syllabification Model of Indonesian Language Named-Entity Using Syntactic n-Gram
Procedia Computer Science, 2021exaly
Indonesian syllabification using a pseudo nearest neighbour rule and phonotactic knowledge
Speech Communication, 2016Agus Harjoko, Sri Hartati
exaly