High speed unknown word prediction using support vector machine for Chinese text-to-speech systems
SCIE
SCOPUS
- Title
- High speed unknown word prediction using support vector machine for Chinese text-to-speech systems
- Authors
- Ha, JH; Zheng, Y; Kim, B; Lee, GG; Seong, YS
- Date Issued
- 2005-01
- Publisher
- SPRINGER-VERLAG BERLIN
- Abstract
- One of the most significant problems in POS (Part-of- Speech) tagging of Chinese texts is an identification of words in a sentence, since there is no blank to delimit the words. Because it is impossible to pre-register all the words in a dictionary, the problem of unknown words inevitably occurs during this process. Therefore, the unknown word problem has remarkable effects on the accuracy of the sound in Chinese TTS (Text-to-Speech) system. In this paper, we present a SVM (support vector machine) based method that predicts the unknown words for the result of word segmentation and tagging. For high speed processing to be used in a TTS, we pre-detect the candidate boundary of the unknown words before starting actual prediction. Therefore we perform a two-phase unknown word prediction in the steps of detection and prediction. Results of the experiments are very promising by showing high precision and high recall with also high speed.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24658
- DOI
- 10.1007/978-3-540-30211-7_54
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3248, page. 509 - 517, 2005-01
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