publication . Article . 2016

Exploiting Bishun to Predict the Pronunciation of Chinese

Chenggang Mi; Yating Yang; Xi Zhou; Lei Wang; Xiao Li; Tonghai Jiang;
Open Access English
  • Published: 30 Sep 2016
  • Publisher: Centro de Investigación en computación, IPN
Abstract
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, w...
Subjects
free text keywords: Pronunciation prediction, Bishun, language model, translation model, error tolerant, Artificial intelligence, business.industry, business, Speech recognition, Chinese speech synthesis, Pronunciation, Pinyin, Natural language processing, computer.software_genre, computer, Chinese characters, Computer science
Related Organizations
20 references, page 1 of 2

1. Hsieh, S.-K. (2006). Concept and Computation: A preliminary survey of Chinese Characters as a Knowledge Resource in NLP. Universität Tübingen.

2. Byrd, R.J. & Tzoukermann, E. (1988). Adapting an English morphological analyzer for French. Proceedings of the 26th annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp. 1-6. DOI: 10.311/982023.982024.

3. Koehn, P., Och, F.J., & Marcu, D. (2003). Statistical phrase-based translation. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1. Association for Computational Linguistics, pp. 48-54. DOI: 10.3115/1073445.1073462.

4. Zens, R., Och, F.J., & Ney, H. (2002). Phrase-based statistical machine translation. Advances in Artificial Intelligence. Springer Berlin Heidelberg, pp. 18-32. DOI: 10.1007/3-540-45751-8_2.

5. Och, F.J. & Ney, H. (2004). The alignment template approach to statistical machine translation. Computational linguistics, Vol. 30, No. 4, pp. 417- 449. DOI: 10.1162/0891201042544884.

6. Shi, X., Knight, K., & Ji, H. (2014). How to Speak a Language without Knowing it.

7. Lin, C.-C. & Tsai, R.T.H. (2012). A Generative Data Augmentation Model for Enhancing Chinese Dialect Pronunciation Prediction. Audio, Speech, and Language Processing, IEEE Transactions on, Vol. 20, No. 4, pp. 1109-1117. DOI: 10.1109/tasl.2011.2172424.

8. Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., & Mercer, R.L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational linguistics, Vol. 19, No. 2, pp. 263- 311.

9. Tran, V.H., Pham, A.T., Nguyen, V.V., Nguyen, H.X., & Nguyen, H.Q. (2015). Parameter Learning for Statistical Machine Translation Using CMA-ES. Knowledge and Systems Engineering. Springer International Publishing, pp. 425-432. DOI: 10.1007/978-3-319-11679-2.

10. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. The Journal of Machine Learning Research, pp.1137-1155.

11. Schwenk, H. (2007). Continuous space language models. Computer Speech & Language, Vol. 21, No. 3, pp. 492-518.

12. Brown, P.F., deSouza, P.V., Mercer, R.L., Pietra, V.J., & Lai, J.C. (1992). Class-based n-gram models of natural language. Computational linguistics, Vol. 18, No. 4, pp, 467-479.

13. Buck, C., Heafield, K., & van Ooyen, B. (2014). Ngram counts and language models from the common crawl. Proceedings of the Language Resources and Evaluation Conference. [OpenAIRE]

14. Singh, A. (2005). The EM Algorithm.

15. Do, C.B. & Batzoglou, S. (2008). What is the expectation maximization algorithm? Nature biotechnology, Vol. 26, No. 8, pp. 897-900. [OpenAIRE]

20 references, page 1 of 2
Abstract
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, w...
Subjects
free text keywords: Pronunciation prediction, Bishun, language model, translation model, error tolerant, Artificial intelligence, business.industry, business, Speech recognition, Chinese speech synthesis, Pronunciation, Pinyin, Natural language processing, computer.software_genre, computer, Chinese characters, Computer science
Related Organizations
20 references, page 1 of 2

1. Hsieh, S.-K. (2006). Concept and Computation: A preliminary survey of Chinese Characters as a Knowledge Resource in NLP. Universität Tübingen.

2. Byrd, R.J. & Tzoukermann, E. (1988). Adapting an English morphological analyzer for French. Proceedings of the 26th annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp. 1-6. DOI: 10.311/982023.982024.

3. Koehn, P., Och, F.J., & Marcu, D. (2003). Statistical phrase-based translation. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1. Association for Computational Linguistics, pp. 48-54. DOI: 10.3115/1073445.1073462.

4. Zens, R., Och, F.J., & Ney, H. (2002). Phrase-based statistical machine translation. Advances in Artificial Intelligence. Springer Berlin Heidelberg, pp. 18-32. DOI: 10.1007/3-540-45751-8_2.

5. Och, F.J. & Ney, H. (2004). The alignment template approach to statistical machine translation. Computational linguistics, Vol. 30, No. 4, pp. 417- 449. DOI: 10.1162/0891201042544884.

6. Shi, X., Knight, K., & Ji, H. (2014). How to Speak a Language without Knowing it.

7. Lin, C.-C. & Tsai, R.T.H. (2012). A Generative Data Augmentation Model for Enhancing Chinese Dialect Pronunciation Prediction. Audio, Speech, and Language Processing, IEEE Transactions on, Vol. 20, No. 4, pp. 1109-1117. DOI: 10.1109/tasl.2011.2172424.

8. Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., & Mercer, R.L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational linguistics, Vol. 19, No. 2, pp. 263- 311.

9. Tran, V.H., Pham, A.T., Nguyen, V.V., Nguyen, H.X., & Nguyen, H.Q. (2015). Parameter Learning for Statistical Machine Translation Using CMA-ES. Knowledge and Systems Engineering. Springer International Publishing, pp. 425-432. DOI: 10.1007/978-3-319-11679-2.

10. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. The Journal of Machine Learning Research, pp.1137-1155.

11. Schwenk, H. (2007). Continuous space language models. Computer Speech & Language, Vol. 21, No. 3, pp. 492-518.

12. Brown, P.F., deSouza, P.V., Mercer, R.L., Pietra, V.J., & Lai, J.C. (1992). Class-based n-gram models of natural language. Computational linguistics, Vol. 18, No. 4, pp, 467-479.

13. Buck, C., Heafield, K., & van Ooyen, B. (2014). Ngram counts and language models from the common crawl. Proceedings of the Language Resources and Evaluation Conference. [OpenAIRE]

14. Singh, A. (2005). The EM Algorithm.

15. Do, C.B. & Batzoglou, S. (2008). What is the expectation maximization algorithm? Nature biotechnology, Vol. 26, No. 8, pp. 897-900. [OpenAIRE]

20 references, page 1 of 2
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publication . Article . 2016

Exploiting Bishun to Predict the Pronunciation of Chinese

Chenggang Mi; Yating Yang; Xi Zhou; Lei Wang; Xiao Li; Tonghai Jiang;