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摘要
En 中文
This paper presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood re-estimation procedure belongings to the class of expectation-maximization algorithms is employed for training the model. Experiments on the Wall Street Journal and Switchboard corpora show improvement in both perplexity and word error rate-word lattice rescoring-over the standard 3-gram language model. (C) 2000 Academic Press.
Keyword:
MAXIMUM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.4
论文数:
1.5K
被引数:
2.6K
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