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NAACL
2003

A Context-Sensitive Homograph Disambiguation in Thai Text-to-Speech Synthesis

8 years 10 months ago
A Context-Sensitive Homograph Disambiguation in Thai Text-to-Speech Synthesis
Homograph ambiguity is an original issue in Text-to-Speech (TTS). To disambiguate homograph, several efficient approaches have been proposed such as part-of-speech (POS) n-gram, Bayesian classifier, decision tree, and Bayesian-hybrid approaches. These methods need words or/and POS tags surrounding the question homographs in disambiguation. Some languages such as Thai, Chinese, and Japanese have no word-boundary delimiter. Therefore before solving homograph ambiguity, we need to identify word boundaries. In this paper, we propose a unique framework that solves both word segmentation and homograph ambiguity problems altogether. Our model employs both local and longdistance contexts, which are automatically extracted by a machine learning technique called Winnow.
Virongrong Tesprasit, Paisarn Charoenpornsawat, Vi
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Virongrong Tesprasit, Paisarn Charoenpornsawat, Virach Sortlertlamvanich
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