NDLI: Traditional Chinese parser and language modeling for Mandadin ASR

Content Provider	IEEE Xplore Digital Library
Author	Ang-Hsing Lin Yih-Ru Wang Sin-Horng Chen
Copyright Year	2013
Description	Author affiliation: Inst. of Commun. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Ang-Hsing Lin) \|\| Dept. of Electr. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Yih-Ru Wang; Sin-Horng Chen)
Abstract	A new approach of traditional Chinese parser to improving the language modeling of Mandarin speech recognition is proposed in this paper. The parser first uses a preprocessing to correct some word segmentation inconsistencies of the text corpus. It then employs a CRF-based word segmentation method and a CRF-based POS tagger to resegment the texts so as to generate better word strings for training an n-gram language model (LM) for ASR. Experimental results on the TCC-300 corpus showed that a word error rate (WER) of 13.4% was achieved by the proposed method. It is about 45% improvement on the relative WER reduction as compared with the previous system.
Starting Page	1
Ending Page	5
File Size	357088
Page Count	5
File Format	PDF
DOI	10.1109/ICSDA.2013.6709889
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Conditional random field Error analysis Language model Speech recognition Tagging Speech Decoding Chinese word segmentation Compounds automatic speech recognition weighted finite state transducer
Content Type	Text
Resource Type	Article

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