Optimized Chinese Pronunciation Prediction byComponent-Based Statistical Machine Translation


Shunle Zhu, Journal of Information Processing Systems Vol. 17, No. 1, pp. 203-212, Feb. 2021  

https://doi.org/10.3745/JIPS.04.0208
Keywords: Chinese Pronunciation Prediction, Component, Features, Statistical Machine Translation (SMT)
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Abstract

To eliminate ambiguities in the existing methods to simplify Chinese pronunciation learning, we propose a model that can predict the pronunciation of Chinese characters automatically. The proposed model relies on a statistical machine translation (SMT) framework. In particular, we consider the components of Chinese characters as the basic unit and consider the pronunciation prediction as a machine translation procedure (the component sequence as a source sentence, the pronunciation, pinyin, as a target sentence). In addition to traditional features such as the bidirectional word translation and the n-gram language model, we also implement a component similarity feature to overcome some typos during practical use. We incorporate these features into a log-linear model. The experimental results show that our approach significantly outperforms other baseline models.


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Cite this article
[APA Style]
Zhu, S. (2021). Optimized Chinese Pronunciation Prediction byComponent-Based Statistical Machine Translation. Journal of Information Processing Systems, 17(1), 203-212. DOI: 10.3745/JIPS.04.0208.

[IEEE Style]
S. Zhu, "Optimized Chinese Pronunciation Prediction byComponent-Based Statistical Machine Translation," Journal of Information Processing Systems, vol. 17, no. 1, pp. 203-212, 2021. DOI: 10.3745/JIPS.04.0208.

[ACM Style]
Shunle Zhu. 2021. Optimized Chinese Pronunciation Prediction byComponent-Based Statistical Machine Translation. Journal of Information Processing Systems, 17, 1, (2021), 203-212. DOI: 10.3745/JIPS.04.0208.