Efficient Normalization Based upon Gpd

In this paper we propose a simple but powerful method for normalizing various sources of mismatch between training and testing conditions in speech recognizers, based on a recent training methodology called the Generalized Probabilistic Descent method (GPD). In this new framework, a gradient based method is used to adapt parameters of the feature extraction process in order to minimize distortion between new speech data and existing classi er models, while most conventional normalization/adaptation methods attempt to adapt classi cation parameters. GPD was proposed as a general discriminative training method for pattern recognizers such as neural networks. Up until now this has been used only for classi er design, sometimes in combination with the design of a non adaptive feature extractor. This paper, in contrast, studies the adaptive training benets of GPD in the framework of normalizing the feature extractor to a new pattern environment. Experiments which use this technique to improve Japanese vowel classi cation were conducted and demonstrate the ability to reduce error rates by as much as 40%.