Exploiting Bishun to Predict the Pronunciation of Chinese

Abstract. Learning to pronounce Chinese characters is usually considered as a very hard part to foreigners to study Chinese. At beginning, Chinese learners must bear in mind thousands of Chinese characters, including their pronunciation, meanings, Bishun (order of strokes) etc., which is very time consuming and boring. In this paper, we proposed a novel method based on translation model to predict the Chinese character pronunciation automatically. We first convert each Chinese character into Bishun, then, we train the pronunciation prediction model (translation model) according to Bishun and their correspondence Pinyin sequences. To make our model practically, we also introduced some error tolerant strategies. Experimental results show that our method can predict the pronunciation of Chinese characters effectively.

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Bibliographic Details
Main Authors: Mi,Chenggang, Yang,Yating, Zhou,Xi, Wang,Lei, Li,Xiao, Jiang,Tonghai
Format: Digital revista
Language:English
Published: Instituto Politécnico Nacional, Centro de Investigación en Computación 2016
Online Access:http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462016000300541
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Summary:Abstract. Learning to pronounce Chinese characters is usually considered as a very hard part to foreigners to study Chinese. At beginning, Chinese learners must bear in mind thousands of Chinese characters, including their pronunciation, meanings, Bishun (order of strokes) etc., which is very time consuming and boring. In this paper, we proposed a novel method based on translation model to predict the Chinese character pronunciation automatically. We first convert each Chinese character into Bishun, then, we train the pronunciation prediction model (translation model) according to Bishun and their correspondence Pinyin sequences. To make our model practically, we also introduced some error tolerant strategies. Experimental results show that our method can predict the pronunciation of Chinese characters effectively.