The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

Background subtraction methods are widely exploited for moving object detection in many applications. A key issue to these methods is how to model and maintain the background correctly and efficiently. This paper describes a foreground detector used in our surveillance system characterized by multiple Gaussian statistics. Compared with the existing methods, our Gaussian mixture model (GMM) diff...

This paper presents the data selection method for speaker recognition. Since there is no promise that more data guarantee better results, the way of data selection becomes important. In the GMM-UBM speaker recognition, the UBM is trained to represent the speaker-independent distribution of acoustic features while the GMM speaker model is tailored for a specific speaker. In this study of data se...

We aim at developing a high-performance system using Gaussian-mixture-model (GMM) supervectors and tree-structured GMMs [6, 7, 8] for the semantic indexing task [1, 2, 3, 4]. GMM supervectors corresponding to six types of audio and visual features are extracted from video shots. Tree-structured GMMs reduce the computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parame...

In this paper, the statistical method of Factor Analysis(FA) is studied on Gaussian Mixture Model(GMM) based speaker identification(SI) system to model the data covariance which is usually neglected due to the training data sparseness. Because the variance of GMM can represents speaker variability, it is very important in SI systems. By FA modeled the data covariance, a relative gain of 39.6% o...

We aim at developing a high-performance semantic indexing system using Gaussian-mixture-model (GMM) supervectors and tree-structured GMMs [1, 2, 3]. GMM supervectors corresponding to six types of audio and visual features are extracted from video shots. Tree-structured GMMs reduce the computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parameters while keeping accurac...

In the recent years the technology for speaker verification or call authentication has received an increasing amount of attention in IVR industry. However due to the complexity of speaker information embedded in the speech signals the current technology still can not produce the verification accuracy to meet the requirement for some applications. In this paper we introduce a new pattern classif...

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of these systems, the posterior probabilities and sufficient statistics calcula...

This paper proposes a new investigation on Gaussian mixture model (GMM) by comparing it with some preliminary experiments on multilayered perceptron network (MLP) with backpropagation learning algorithm (BKP) and dynamic time warping (DTW) techniques on Thai text-dependent speaker identification system. Three major identification engines are conducted on 50 speakers with isolated digits 0-9. Tr...

In Audio fingerprinting, a song must be recognized by matching an extracted fingerprint to a database of previously computed fingerprints. One of the key issues in fingerprinting is the generation of fingerprints that provide discrimination among different songs and at the same time invariant to the distorted versions of the same song. In this paper, we evaluate various features such as spectra...