About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Journal of Computational and Graphical Statistics
Paper
Power exponential densities for the training and classification of acoustic feature vectors in speech recognition
Abstract
We consider a parametric family of multivariate density functions formed by mixture models from univariate functions of the type exp(–|x|α) for modeling acoustic feature vectors used in automatic recognition of speech. The parameter α is used to measure the non-Gaussian nature of the data. Previous work has focused on estimating the mean and the variance of the data for a fixed α. Here we attempt to estimate the α from the data using a maximum likelihood criterion. Among other things, we show that there is a balance between α and the number of data pointsNthat must be satisfied for efficient estimation. Numerical experiments are performed on multidimensional vectors obtained from speech data. © 2001 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.