Succeeding Word Prediction for Speech Recognition Based on Stochastic Language Model
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概要
- 論文の詳細を見る
For the purpose of automatic speech recognition, language models (LMs) are used to predict possible succeeding words for a given partial word sequence and thereby to reduce the search space. In this paper several kinds of stochastic language models (SLMs) are evaluated-bigram, trigram, hidden Markov model (HMM), bigram-HMM, stochastic context-free grammar (SCFG) and hand-written Bunsetsu Grammar. To compare the predictive power of these SLMs, the evaluation was conducted from two points of views: (1) relationship between the number of model parameters and entropy (2) predictive rate of succeeding part of speech (POS) and succeeding word. We propose a new type of bigram-HMM and compare it with the other models. Two kinds of approximations are tried and examined through experiments. Results based on both of English Brown-Corpus and Japanese ATR dialog database showed that the extended bigram-HMM had better performance than the others and was more suitable to be a language model.
- 社団法人電子情報通信学会の論文
- 1996-04-25
著者
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Nakagawa Seiichi
Department Of Information And Computer Sciences Toyohashi University Of Technology
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Nakagawa Seiichi
Department Of Information And Computer Sciences Toyohashi University
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ZHOU Min
Department of Chemistry, Shandong University
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Zhou Min
Department Of Chemistry Shandong University
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Zhou Min
Department Of Information And Computer Sciences Toyohashi University Of Technology
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Nakagawa Seiichi
Department of Computer Science and Engineering, Toyohashi University of Technology
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