Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the...

The large-sample properties of likelihood-based statistical inference under mixture models have received much attention from statisticians. Although the consistency of the nonparametric MLE is regarded as a standard conclusion, many researchers ignore the precise conditions required on the mixture model. An incorrect claim of consistency can lead to false conclusions even if the mixture model u...

We propese an exact maximum likelihood (ML) approach for hidden Markov modeling of speech signals using models with mixtures of Gaussian autoregressive (AR) output probability distributions. This approach differs from the commonly used approach in two aspects. First, the parameters of the AR models are calculated using the exact, rather than the asymptotic, form of the likelihood function. Seco...

Modern speech recognizers rely on three core components: an acoustic model, a language model, and a pronunciation lexicon. In order to expand speech recognition capability to lowresource languages and domains, techniques to peel away the expert knowledge required to craft these three components have been growing in popularity. In this paper, we present a method for automatically learning a weig...