Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated ...

In this paper, a discriminative training procedure for a Gaussian Mixture Model (GMM) language identification system is described. The proposal is based on the Generalized Probabilistic Descent (GPD) algorithm and Minimum Classification Error Rates formulated to estimate the GMM parameters. The evaluation is conducted using the OGI multi-language telephone speech corpus. The experimental result...

Point-of-Interest (POI) recommendation is a significant service for location-based social networks (LBSNs). It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users’ interests depends on three factors: user preference, social influence, and geographical influence. Hence extracting the information from users’ check-in records is the ...

This paper presents BUT system submitted to NIST 2008 SRE. It includes two subsystems based on Joint Factor Analysis (JFA) GMM/UBM and one based on SVM-GMM. The systems were developed on NIST SRE 2006 data, and the results are presented on NIST SRE 2008 evaluation data. We concentrate on the influence of side information in the calibration.

Speech recognition is the process of converting speech signals into words. For acoustic modeling HMM-GMM is used for many years. For GMM, it requires assumptions near the data distribution for calculating probabilities. For removing this limitation, GMM is replaced by DNN in acoustic model. Deep neural networks are the feed forward neural networks having more than one or multiple layers of hidd...

Personal identity identification is an important requirement for controlling access to protected resources. Biometric identification by using certain features of a person is a more secured solution for security identification. Advances in speech processing technology and digital signal processors have made possible the design of high-performance and practical speaker recognition systems. A more...

In this report we address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client’s frontal face model with artificially synthesized models for non-frontal views. In the framework of a Gaussian Mixture Model (GMM) based classifier, two techniques are proposed for the synthesis: UBMdiff and LinReg. Both tech...

This paper studies the reliance of a Gaussian Mixture Model (GMM) based closed-set Speaker Identification system on model convergence and describes methods to improve this convergence. It shows that the reason why the Vector Quantisation GMMs (VQGMMs) outperform a simple GMM is mainly due to decreasing the complexity of the data during training. In addition, it is shown that the VQGMM system is...

This paper establishes the almost sure convergence and asymptotic normality of levels and differenced quasi maximum-likelihood (QML) estimators of dynamic panel data models. The QML estimators are robust with respect to initial conditions, conditional and time-series heteroskedasticity, and misspecification of the log-likelihood. The paper also provides an ECME algorithm for calculating levels ...

We were focusing on the core test. One primary and two contrastive systems were submitted. Primary system was a fusion of all GMM-UBM, SVM-GSV and SVM-GLDS systems described in this paper. The first contrastive system was a fusion of GMM-UBM based systems described in Section 3.1, the next was a fusion of SVM based systems described in Section 3.2. All the submitted scores can be interpreted as...