2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13（2006）
LIA
被引用8|浏览16
摘要
A method is described for predicting acoustic feature variability by analyzing the consensus and relative entropy of phoneme posterior probability distributions obtained with different acoustic models having the same type of observations. Variability prediction is used for diagnosis of automatic speech recognition (ASR) systems. When errors are likely to occur, different feature sets are considered for correcting recognition results. Experimental results are provided on the CH1 Italian portion of AURORA3
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关键词
speech recognition,statistical distributions,AURORA3,CH1 Italian portion,acoustic feature variability prediction,acoustic models,automatic speech recognition systems,consensus analysis,feature variability characterization,phoneme posterior probability distributions,relative entropy analysis
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