The goal of image segmentation is to cluster the pixels of an image into several regions. This article describes the method of image segmentation using Artificial Bee Colony Optimization (ABC). This optimization technique is motivated by intelligent behaviour of honey bees and it provides a population based search procedure. In this article Gaussian Mixture Model (GMM) is used and each pixel cl...

One-to-many eigenvoice conversion (EVC) allows the conversion of a specific source speaker into arbitrary target speakers. Eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets consisting of the source speaker and many pre-stored target speakers. The EV-GMM is adapted for arbitrary target speakers using only a few utterances by estimating a small numb...

An automatic Language Identification (LID) is the task of automatically recognizing a language from the given spoken utterance. Language identification is used to identify the language of the particular audio and reduce the complexity of the audio sample. LID systems that rely on multiple language phone recognition language modeling (PRLM) and n-gram language modeling produces the best performa...

A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing tim...

In a previous work, Multi-Environment Model based LInear Normalization, MEMLIN, was presented and it was proved to be effective to compensate environment mismatch. MEMLIN is an empirical feature vector normalization which models clean and noisy spaces by Gaussian Mixture Models (GMMs). In this algorithm, the probability of the clean model Gaussian, given the noisy model one and the noisy featur...

Robust stochastic modeling of speech is an important issue for the performance of practical applications. The Gaussian mixture model, GMM, is widely used in speaker ID, but its performance would get limited in the presence of unseen noise and distortions. Such noisy data, referred to as ”outliers” for the original distribution, can be better represented by the use of heavy-tail distributions, s...

Most techniques for speaker verification today use Gaussian Mixture Models (GMMs) and make the decision by comparing the likelihood of the speaker model to the likelihood of a universal background model (UBM). The paper proposes to replace the UBM by an individual background model (IBM) that is generated for each speaker. The IBM is created using the K-nearest cohort models and the UBM by a sim...

This paper introduces the technique of anchor modeling in the applications of speaker detection and speaker indexing. The anchor modeling algorithm is refined by pruning the number of models needed. The system is applied to the speaker detection problem where its performance is shown to fall short of the state-of-the-art Gaussian Mixture Model with Universal Background Model (GMM-UBM) system. H...

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...