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The use of a graph embedding framework is investigated as a regularization technique in the expectation-maximization (EM) algorithm applied to automatic speech recognition (ASR). The technique is motivated by the fact that graph em-beddings of feature vectors have been shown to provide useful characterizations of the underlying manifolds on which these features lie. Incorporating intrinsic graphs...
This paper presents a family of discriminative manifold learning approaches to feature space dimensionality reduction in noise robust automatic speech recognition (ASR). The specific goal of these techniques is to preserve local manifold structure in feature space while at the same time maximizing the separability between classes of feature vectors. In the manifold space, the relationships among the...
This paper considers the application of discriminative manifold learning approaches in feature analysis for automatic speech recognition (ASR). The issue of manifold learning is addressed for feature space dimensionality reduction in domains involving noise corrupted speech. The locality preserving discriminant analysis (LPDA) approach to manifold learning is investigated. This class of techniques...
This paper presents a comparison of three techniques for dimensionally reduction in feature analysis for automatic speech recognition (ASR). All three approaches estimate a linear transformation that is applied to concatenated log spectral features and provide a mechanism for efficient modeling of spectral dynamics in ASR. The goal of the paper is to investigate the effectiveness of a discriminative...
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