Abstract
One of the major research thrusts in the speech group at ICSI is to use Multi-Layer Perceptron (MLP) based features in automatic speech recognition (ASR). This paper presents a study of three aspects of this effort: 1) the properties of the MLP features which make them useful, 2) incorporating MLP features together with PLP features in ASR, and 3) possible redundancy between MLP features and more conventional system refinements such as discriminative training and system combination. The paper shows that MLP transformations yield variables that have regular distributions, which can be further modified by using logarithm to make the distribution easier to model by a Gaussian-HMM. Two or more vectors of these features can easily be combined without increasing the feature dimension. […]
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