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SPEECH RECOGNITION DEVICE, WEIGHT VECTOR LEARNING DEVICE, SPEECH RECOGNITION METHOD, WEIGHT VECTOR LEARNING METHOD, AND PROGRAM
SPEECH RECOGNITION DEVICE, WEIGHT VECTOR LEARNING DEVICE, SPEECH RECOGNITION METHOD, WEIGHT VECTOR LEARNING METHOD, AND PROGRAM
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机译:语音识别装置,体重向量学习装置,语音识别方法,体重向量学习方法以及程序
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摘要
PROBLEM TO BE SOLVED: To optimize a speech recognition parameter using a WFST network expression.;SOLUTION: A speech recognition device includes a recording part, a WFST synthesis part, a feature quantity extraction part, a WFST-type log-linear decoder, and an output symbol extraction part. The recording part records a pronunciation dictionary model, a language model, a sound model, and a weight vector α. The WFST synthesis part synthesizes the pronunciation dictionary model, the language model, and the sound model, and outputs a WFST network. The WFST-type log-linear decoder expresses a score W (X, A) of an arc series A in the log domain when a time series of a feature quantity vector is given, by linear representation of a feature vector ϕ (X, A) acquired from the time series X of the feature quantity vector and the arc series A and the weight vector α, and outputs an arc series of the highest score. The output symbol extraction part determines a word sequence to the arc series and outputs it.;COPYRIGHT: (C)2011,JPO&INPIT
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