Controlling the Complexity of HMM Systems by Regularization

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Controlling the Complexity of HMM Systems by Regularization
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the EM training method by a penalty term that favors simple and smooth HMM systems. The penalty term is constructed as a mixture model of negative exponential distributions that is assumed to generate the state dependent emission probabilities of the HMMs. This new method is the successful transfer of a well known regularization approach in neural networks to the HMM domain and can be interpreted as a generalization of traditional state-tying for HMM systems. The effect of regularization is demonstrated for continuous speech recognition tasks by improvingoverfitted triphone models and by speaker adaptation with limited training data.
Christoph Neukirchen, Gerhard Rigoll
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where NIPS
Authors Christoph Neukirchen, Gerhard Rigoll
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