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Feature transformations are commonly used in speech recognition to account for distribution mismatches between the source and target domains (also referred to as covariate shift). Linear (affine) or piecewise linear transformations are typically considered. In this paper, we present deep neural network (DNN) based nonlinear feature transformations estimated under the maximum likelihood criterion....
We present novel bounds on the classification error which are based on the f-Divergence and, at the same time, can be used as practical training criteria. There exist virtually no studies which investigate the link between the f-Divergence, the classification error and practical training criteria. So far only the Kullback-Leibler f-Divergence has been examined in this context to formulate a bound...
Minimum phone error (MPE) training of deep neural networks (DNN) is an effective technique for reducing word error rate of automatic speech recognition tasks. This training is often carried out using a Hessian-free (HF) quasi-Newton approach, although other methods such as stochastic gradient descent have also been applied successfully. In this paper we present a novel stochastic approach to HF sequence...
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