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- W3141175705 abstract "Speech conversion can be considered as the most comfortable means of information transmission between the human and the machine. To realize such a mechanism, there must be a technique for efficient natural language processing.This paper proposes a new topic-control mechanism based on the statistical technique as a means to ameliorate the complexity of the natural language processing. The input speech is analyzed using the predictive CFG. The sequence of generated grammatical rules in used to drive HMM, and the topic is identified as the bias of the state distribution. Conversely, the bias of the distribution is reflected dynamically on the probabilities of the grammatical rules, thus, recognition search space is narrowed. Using the large-scale dialogue database, the reduction effect for the perplexity to the text data input is evaluated, and it is verified that the proposed method has a high power to narrow the search space." @default.
- W3141175705 created "2021-04-13" @default.
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- W3141175705 date "1995-01-01" @default.
- W3141175705 modified "2023-09-23" @default.
- W3141175705 title "Topic focusing mechanism for speech recognition based on probabilistic grammar and topic Markov model." @default.
- W3141175705 hasPublicationYear "1995" @default.
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