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Succeeding Word Prediction for Speech Recognition Based on Stochastic Language Model
Min ZHOU Seiichi NAKAGAWA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1996/04/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Speech Processing and Acoustics
stochastic language model, bigram, trigram, HMM, bigram-HMM, context-free grammar, entropy, perplexity, prediction rate,
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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.