NICT/ATR Chinese-Japanese-English Speech-to-Speech Translation System

被引:12
作者
Shimizu, Tohru [1 ]
Ashikari, Yutaka [1 ]
Sumita, Eiichiro [1 ]
Zhang, Jinsong [1 ]
Nakamura, Satoshi [1 ]
机构
[1] Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology, ATR Spoken Language Translation Research Laboratories, Kyoto, 619-0288
关键词
large-scale corpus; machine translation; speech recognition; speech synthesis; speech-to-speech translation;
D O I
10.1016/S1007-0214(08)70086-5
中图分类号
学科分类号
摘要
This paper describes the latest version of the Chinese-Japanese-English handheld speech-to-speech translation system developed by NICT/ATR, which is now ready to be deployed for travelers. With the entire speech-to-speech translation function being implemented into one terminal, it realizes real-time, location-free speech-to-speech translation. A new noise-suppression technique notably improves the speech recognition performance. Corpus-based approaches of speech recognition, machine translation, and speech synthesis enable coverage of a wide variety of topics and portability to other languages. Test results show that the character accuracy of speech recognition is 82%-94% for Chinese speech, with a bilingual evaluation understudy score of machine translation is 0.55-0.74 for Chinese-Japanese and Chinese-English. © 2008 Tsinghua University Press.
引用
收藏
页码:540 / 544
页数:4
相关论文
共 6 条
[1]  
Nakamura S., Markov K., Nakaiwa H., Et al., The ATR multilingual speech-to-speech translation system, IEEE Trans. on Audio, Speech, and Language Processing, 14, 2, pp. 365-376, (2006)
[2]  
Fujimoto M., Nakamura S., A non-stationary noise suppression method based on particle filtering and polyak averaging, IEICE Transactions on Information and Systems, J89-ED, 3, pp. 922-930, (2006)
[3]  
Arulampalam M., Maskell S., Gordon N., Et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. on Signal Processing, 50, 2, pp. 174-188, (2002)
[4]  
Yamamoto H., Isogai S., Sagisaka Y., Multi-class composite N-gram language model, Speech Communication, 41, pp. 369-379, (2003)
[5]  
Second International Chinese Word Segmentation Bakeoff
[6]  
NIST 2005 Machine Translation Evaluation Official Results