NDLI: A smoothing algorithm for the task adaptation Chinese Trigram model

Content Provider	IEEE Xplore Digital Library
Author	Jiang Minghu Yuan Baozong Lin Biqin Tang Xiaofang
Copyright Year	1998
Description	Author affiliation: Inst. of Inf. Sci., Northern Jiaotong Univ., Beijing, China (Jiang Minghu)
Abstract	The paper mainly solves two problems. A Chinese trigram model of task adaptation ability is set up. A zerogram to trigram probability statistics information base of the 1994 "People Daily" are built, making use of the successful experience of using HMM in speech recognition, and the adopted Baum-Welch algorithm for optimisation of the weights. Each weight stands for the correlation statistic reliability of these models. The probability statistics matrix smoothing algorithm of the parameter space is carried out, in order to offset the matrix sparse data of statistic probability. The "People Daily" corpus statistic results are regarded as the preliminary statistic results. When changing the application domain, the recognition accuracy rate of the preliminary statistic results decline, and we adopt "PC World" as the corpus of the changing domain and carry out successive training, then a second smoothing of the preliminary statistic results, and the successive statistic results are looked upon as the final results. A trigram model of task adaptation is obtained. The experimental results show this language model reduces the workload of successive training, and can effectively reduce the perplexity of language models in the task changing domain. It has a higher language adaptation ability in the task changing domain.
Starting Page	738
Ending Page	741
File Size	257886
Page Count	4
File Format	PDF
ISBN	0780343255
DOI	10.1109/ICOSP.1998.770317
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Smoothing methods Adaptation model Statistics Probability Hidden Markov models Speech recognition Information science Sparse matrices Natural languages Entropy
Content Type	Text
Resource Type	Article

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