Mixture modeling, which considers the potential heterogeneity in data, is widely adopted for classification and clustering problems. models can be estimated using Expectation-Maximization algorithm, works with complete estimating equations conditioned by latent membership variables of cluster assignment based on hierarchical expression mixture models. However, when components have light tails s...

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low di...

Variational KL (varKL) divergence minimization was previously applied to restructuring acoustic models (AMs) using Gaussian mixture models by reducing their size while preserving their accuracy. In this paper, we derive a related varKL for exponential family mixture models (EMMs) and test its accuracy using the weighted local maximum likelihood agglomerative clustering technique. Minimizing var...

Gaussian Mixture Models (GMM) have been broadly applied for the fitting of probability density function. However, due to the intrinsic linearity of GMM, usually many components are needed to appropriately fit the data distribution, when there are curve manifolds in the data cloud. In order to solve this problem and represent data with curve manifolds better, in this paper we propose a new nonli...

It is well known that speaker variability caused by accent is an important factor in speech recognition. Some major accents in China are so different as to make this problem very severe. In this paper, we propose a Gaussian mixture model (GMM) based Mandarin accent identification method. In this method, a number of GMMs are trained to identify the most likely accent given test utterances. The i...

The current paper proposes skew Gaussian mixture models for speaker recognition and an associated algorithm for its training from experimental data. Speaker identification experiments were conducted, in which speakers were modeled using the familiar Gaussian mixture models (GMM), and the new skewGMM. Each model type was evaluated using two sets of feature vectors, the mel-frequency cepstral coe...

This paper explores the topic of voice conversion as explored in a joint project with Percy Liang (EECS, Berkeley). For our purposes, voice conversion is the process of modifying the speech signal of one speaker (source) so that it sounds as thought it had been pronounced by a different speaker (target). By using a Gaussian mixture model (GMM) to model the features of the source speaker, we can...

Training the parameters of statistical models to describe a given data set is a central task in the field of data mining and machine learning. A very popular and powerful way of parameter estimation is the method of maximum likelihood estimation (MLE). Among the most widely used families of statistical models are mixture models, especially, mixtures of Gaussian distributions. A popular hard-clu...

We present a novel method for representing “extruded” distributions. An extruded distribution is an M -dimensional manifold in the parameter space of the component distribution. Representations of that manifold are “continuous mixture models”. We present a method for forming one-dimensional continuous Gaussian mixture models of sampled extruded Gaussian distributions via ridges of goodness-of-#...