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Power Exponential Densities for the Training and Classification of Acoustic Feature Vectors in Speech Recognition

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 points N that must be satisfied for efficient estimation. Numerical experiments are performed on multidimensional vectors obtained from speech data.