In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple

Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select

an adaptive skin color model based on the Gaussian mixture model to handle the changing conditions. Initial estimation of the number and weights of skin color clusters are obtained using a modified form of the general Expectation maximization algorithm, The model adapts to changes in imaging conditions

This paper presents a comparative analysis of the performance of decoupled and adapted Gaussian mixture models (GMMs) for open-set, text-independent speaker identification (OSTI-SI). The analysis is based on a set of experiments using an appropriate subset of the NIST-SRE 2003 database and vari

in including the complete details regarding the web log data.To overcome this disadvantage, in this paper a model based on Adaptive Gaussian Mixture Model, an extension of Gaussian Mixture Model (GMM) to interpret the user navigation behavior is brought out. The proposed model is applied on user traffic data

Abstract—Recently, we introduced the sorted Gaussian mixture models (SGMMs) algorithm providing the means to tradeoff per-formance for operational speed and thus permitting the speed-up of GMM-based classification schemes. The performance of the SGMM algorithm depends on the proper choice

applied to Gaussian Mixtures. Our approach is fully automatic in the choice of the number of mixture components, the initialization parameters and the stopping criterion. The rationale is to start with a single Gaussian in the mixture, covering the whole data set, and split it incrementally during

filtering as a frequency decomposition into bands, and analyze the influence of each band sepa-rately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor fil-tering with Gaussian Mixture Model (GMM) modelling. Fi-nally, this new system is evaluated

Gabor filtering as a frequency decomposition into bands, and analyze the influence of each band separately for face recognition. Then, a new face verification scheme is proposed, combining the strengths of Gabor filtering with Gaussian Mixture Model (GMM) modelling. Finally, this new system is evalu

Abstract—A new approach to speaker change detection is proposed and investigated. The method, which is based on a probabilistic framework, provides an effective means for tackling the problem posed by phonetic variation in high-resolution speaker change detection. Additionally, the approach incorporates the capability for dealing with undesired effects of variations in speech characteristics. Using the experimental investigations conduced with clean and broadcast news audio, it is shown that the proposed method is significantly more effective than the currently popular techniques for speaker change detection. To enhance the computational efficiency of the proposed method, modified implementation algorithms are introduced which are based on the exploitation of the redundant operations and a fast scoring procedure. It is shown that, through the use of the proposed fast algorithm, the computational efficiency of the approach can be increased by over 77 % without significant reduction in its accuracy. The paper discusses the principles and characteristics of the proposed speaker change detection method, and provides a detailed description of its efficient implementation. The experiments, investigating the performance of the proposed method and its effectiveness in relation to other approaches, are described and an analysis of the results is presented. Index Terms—Bilateral scoring, phonetic heterogeneity, probabilistic approach. I.