In this paper, we improve our previous cluster model selection method for agglomerative hierarchical speaker clustering (AHSC) based on incremental Gaussian mixture models (iGMMs). In the previous work, we measured the likelihood of all the data points in a given cluster for each mixture component of the GMM modeling the cluster. Then, we selected the N -best component Gaussians with the highes...

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the ...

9 Traffic speed and length-based vehicle classification data are critical inputs for traffic operations, 10 pavement design and maintenance, and transportation planning. However, they cannot be 11 measured directly by single-loop detectors, the most widely deployed type of traffic sensor in the 12 existing roadway infrastructure. In this study, a Gaussian Mixture Model (GMM)-based 13 approach i...

In this paper, we propose a noise robust speech recognition method by combination of temporal domain singular value decomposition(SVD) based speech enhancement and Gaussian mixture model(GMM) based speech estimation. The bottleneck of GMM based approach is a noise estimation problem. For this noise estimation problem, we incorporated the adaptive noise estimation in GMM based approach. Furtherm...

The EL estimators have some favorable higher order asymptotic properties. We extend the EL method proposed by Donald et al. (2003) to estimate non-i:i:d: continuous-time models with the known functional form of the conditional characteristic function. In many cases even the MLE method can not be performed, the EL method can do. More over, not only does the EL method resolve the problem of covar...

Subspace Gaussian mixture model(GMM) is an alternative approach to approximate the probabilistic density function (p.d.f) of a set of independent identical distributed (i.i.d) data with prior density estimates. In this approach, the prior density of GMM parameters is estimated from a development dataset, and when predict the new enrolled data, the prior knowledge can be utilised by criteria lik...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components in speech decoding. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performance ...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components of speech recognition. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performan...

This paper describes a method for robust offline writer identification. We propose to use RootSIFT descriptors computed densely at the script contours. GMM supervectors are used as encoding method to describe the characteristic handwriting of an individual scribe. GMM supervectors are created by adapting a background model to the distribution of local feature descriptors. Finally, we propose to...