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Abstract:
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 improving overfitted triphone models and by
speaker adaptation with limited training data.
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