In an HMM based large vocabulary continuous speech recognition system, the evaluation of context dependent acoustic models is very time consuming. In Semi-Continuous HMMs, a state is modelled as a mixture of elementary generally gaussian probability density functions. Observation probability calculations of these states can be made faster by reducing the size of the mixture of gaussians used to...

The Gaussian mixture model-universal background model (GMM-UBM) has been dominant in text-independent speaker recognition tasks. However the conventional GMM-UBM method assumes that each Gaussian mixture is independent and ignores the fact that within Gaussian mixtures, there do exist some useful high-level speaker-dependent characteristics, such as word usage or speaking habits. Based on the G...

Using only spatially and temporally replicated point counts, Royle (2004b, Biometrics 60, 108-115) developed an N-mixture model to estimate the abundance of an animal population when individual animal detection probability is unknown. One assumption inherent in this model is that the animal populations at each sampled location are closed with respect to migration, births, and deaths throughout ...

Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use probability models for cluster analysis. However, the corresponding probability distributions to most clus...

in this work, thermal degradation behavior of a fuel-rich energetic mixture containing epoxy binder was studied by thrmogravimetric analysis and differential scanning calorimetry under dynamic nitrogen atmosphere at different heating rates. variation of the thermal degradation activation energy of the mixture was evaluated by differential and integral isoconversional methods via akts software p...

Fertility rate is one of the most important global indexes. Past researchers found models which fit to age-specific fertility rates. For example, mixture probability density functions have been proposed for situations with bi-modal fertility patterns. This model is less useful for unimodal age-specific fertility rate patterns, so a model based on skew-symmetric (skew-normal) pdf was proposed by...

Pharmacokinetic/pharmacodynamic phenotypes are identified using nonlinear random effects models with finite mixture structures. A maximum a posteriori probability estimation approach is presented using an EM algorithm with importance sampling. Parameters for the conjugate prior densities can be based on prior studies or set to represent vague knowledge about the model parameters. A detailed sim...

We develop a quantum probability model that can account for situations where people’s causal judgments violate the properties of causal Bayes nets and demonstrate how the parameters of our model can be interpreted to provide information about underlying cognitive processes. We implement this model within a hierarchical Bayesian inference framework that allows us to systematically identify indiv...

In this paper we provide an explicit probability distribution for classification purposes when observations are viewed on the real line and classifications are to be based on numerical orderings. The classification model is derived from a Bayesian nonparametric mixture of Dirichlet process model; with some modifications. The resulting approach then more closely resembles a classical hierarchica...

This work presents a noise spectrum estimator based on the Gaussian mixture model (GMM)-based speech presence probability (SPP) for robust speech recognition. Estimated noise spectrum is then used to compute a subband a posteriori signal-to-noise ratio (SNR). A sigmoid shape weighting rule is formed based on this subband a posteriori SNR to enhance the speech spectrum in the auditory domain, wh...