We are interested in making inference on the AR parameter in an AR(1) panel data model when the time-series dimension is …xed and the cross-section dimension is large. We consider a GMM estimator based on the second order moments of the observed variables. It turns out that when the AR parameter equals unity and certain restrictions apply to the other parameters in the model then local identi…c...

In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the propo...

In this paper, we propose a novel frequency-domain approach to double-talk detection (DTD) based on the Gaussian mixture model (GMM). In contrast to a previous approach based on a simple and heuristic decision rule utilizing time-domain crosscorrelations, GMM is applied to a set of feature vectors extracted from the frequency-domain cross-correlation coefficients. Performance of the proposed ap...

Standard Gaussian mixture modelling does not possess time sequence information (TSI) other than that which might be embedded in the acoustic features. Dynamic time warping relates directly to TSI, time-warping two sequences of features into alignment. Here, a hybrid system embedding DTW into a GMM is presented. Improved automatic speaker verification performance is demonstrated. Testing 1000 sp...

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...

Urban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs higher computational cost. This paper proposes novel framework that combines descriptive strength high-performance classification capabilities...

The article describes a speaker recognition system based on continuous speech using GMM multivariate probability distributions. A theoretical model of the system including the extraction of distinctive features and statistical modeling is described. The efficiency of the system implemented in the Linux operating system was determined. The system is designed to support the functionality of the P...

This paper shows that Hidden Markov Models (HMMs) can be effectively applied to 3D face data. The examined HMM techniques are shown to be superior to a previously examined Gaussian Mixture Model (GMM) technique. Experiments conducted on the Face Recognition Grand Challenge database show that the Equal Error Rate can be reduced from 0.88% for the GMM technique to 0.36% for the best HMM approach.

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...

It has been previously demonstrated that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition. Recently, a simpler generative model, namely the Gaussian Mixture Model (GMM), was also shown to perform well. In most of the previous literature related to these models, the experiments we...