In This paper presents an overview of a stateof-the-art text-independent speaker verification system. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization use...

Relevance Feedback (RF) has become a powerful technique in content-based image retrieval. Most RF methods assume that positive images follow the single Gaussian distribution, which is not sufficient to model the actual distribution of images due to the gap between the semantic concept and low-level features. In this paper, Gaussian mixture model (GMM) is applied to represent the distribution of...

Although typical model-based noise suppression including the vector Taylor series-based approach employs a single Gaussian distribution for the noise model, it is insufficient for nonstationary noises which have a complex structured distribution. As a solution to this problem, we have already proposed a method for estimating a Gaussian mixture model (GMM)-based noise model by using a minimum me...

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 ...

Gaussian Mixture Models (GMM) have been broadly applied for the fitting of probability density function. However, due to the intrinsic linearity of GMM, usually many components are needed to appropriately fit the data distribution, when there are curve manifolds in the data cloud. In order to solve this problem and represent data with curve manifolds better, in this paper we propose a new nonli...

In this section, we provide details on the toy example presented in Section 1. We used the following simple experimental setting. All covariances for the bidimensional distributions are diagonal, therefore all bidimensional Gaussian distributions are parametrised by 4 parameters (μ1, μ2, σ1, σ2) where μ, σ is a mean-variance pair on each dimension. We consider a data distribution that is a mixt...

This paper presents a new spectral modeling and conversion method for voice conversion. In contrast to the conventional Gaussian mixture model (GMM) based methods, we use restricted Boltzmann machines (RBMs) as probability density models to model the joint distributions of source and target spectral features. The Gaussian distribution in each mixture of GMM is replaced by an RBM, which can bett...

In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood functi...

This paper presents a new approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). The MSD-GMM allows us to model continuous pitch values for voiced frames and discrete symbols representing unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled...

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 ...