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Method and system for selectively biased linear discriminant analysis in automatic speech recognition systems
Method and system for selectively biased linear discriminant analysis in automatic speech recognition systems
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机译:自动语音识别系统中选择性偏向线性判别分析的方法和系统
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摘要
A system and method are presented for selectively biased linear discriminant analysis in automatic speech recognition systems. Linear Discriminant Analysis (LDA) may be used to improve the discrimination between the hidden Markov model (HMM) tied-states in the acoustic feature space. The between-class and within-class covariance matrices may be biased based on the observed recognition errors of the tied-states, such as shared HMM states of the context dependent tri-phone acoustic model. The recognition errors may be obtained from a trained maximum-likelihood acoustic model utilizing the tied-states which may then be used as classes in the analysis.
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