An automated method of MR brain image segmentation is presented. A block based Expectation Maximization Algorithm is proposed for the tissue classification of MR brain images. The standard Gaussian Mixture Model is the most widely used method for MR Brain image segmentation and Expectation Maximization algorithm is used to estimate the model parameters. The Gaussian Mixture Model considers each...

In this paper, we present a new method for isolated keyword detection that is meant to activate a personal device from standby state. Instead of using the common method for speech recognition such as Hidden Markov Model (HMM) or Dynamic Time Warping (DTW), we modify a GMM-UBM (Gaussian Mixture Model – Universal Background Model) scheme that is better known in speaker recognition field. Since on...

In this project, I propose the application of a discriminative framework for segmentation of a T1weighted magnetic resonance image(MRI). The use of Gaussian mixture models (GMM) is fairly ubiquitous in processing brain images for statistical analysis. This generative framework makes several assumptions that restricts its success and application. GMM assumes there is no spatial correlation when ...

This paper describes a method for determining the vocal tract spectrum from articulatory movements using a Gaussian Mixture Model (GMM) to synthesize speech with articulatory information. The GMM on joint probability density of articulatory parameters and acoustic spectral parameters is trained using a parallel acousticarticulatory speech database. We evaluate the performance of the GMM-based m...

Fitting a Gaussian mixture model (GMM) to the smoothed speech spectrum allows an alternative set of features to be extracted from the speech signal. These features have been shown to possess information complementary to the standard MFCC parameterisation. This paper further investigates the use of these GMM features in combination with MFCCs. The extraction and use of a confidence metric to com...

Gaussian Mixture Models (GMMs) in combination with Support Vector Machine (SVM) classifiers have been shown to give excellent classification accuracy in speaker recognition. In this work we use this approach for language identification, and we compare its performance with the standard approach based on GMMs. In the GMM-SVM framework, a GMM is trained for each training or test utterance. Since i...

OBJECTIVE The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker gen...

We propose a low-memory-bandwidth, high-efficiency VLSI architecture for 60-k word real-time continuous speech recognition. Our architecture includes a cache architecture using the locality of speech recognition, beam pruning using a dynamic threshold, two-stage language model searching, a parallel Gaussian Mixture Model (GMM) architecture based on the mixture level and frame level, a parallel ...

It is often useful to fit a probability model to a data collection, in order to concisely represent the data, to feed learning algorithms that work on densities, to extract features or, simply, to uncover underlying structures. A particularly popular probability model is the Gaussian Mixture Model (GMM). Among many other applications, GMM form a central tool to build time-frequency models of au...

In this article we present an efficient approach to modeling the acoustic features for the tasks of recognizing various paralinguistic henomena. Instead of the standard scheme of adapting the Universal Background Model (UBM), represented by the Gaussian ixture Model (GMM), normally used to model the frame-level acoustic features, we propose to represent the UBM by building monophone-based Hidde...