Semi-supervised learning of speech sounds
نویسندگان
چکیده
Recently, there has been much interest in both semi-supervised and manifold learning algorithms, though their applicability has not been explored for all domains. This paper has two goals: (i) to demonstrate semi-supervised approaches based solely on clustering are insufficient for phoneme classification and (ii) to present a new manifold-based semi-supervised algorithm to remedy this shortcoming. The improved performance of our approach over cluster-based methods substantiates the practical relevance of a geometric perspective on speech sounds.
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A Geometric Perspective on Speech Sounds
In order to effectively approach high dimensional pattern recognition problems, one seeks to understand and exploit any inherent low dimensional structure. Recently, a number of manifold learning algorithms have been motivated by a geometric point of view that models high dimensional data as lying near a low dimensional submanifold of the original space. Our paper has two main goals: (i) to inv...
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