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...

The voice conversion algorithm based on the Gaussian mixture model (GMM) has also been proposed by Stylianou et al. In this algorithm, the acoustic space of a speaker is represented continuously. In this paper, we apply this GMMbased voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order to evaluate this voice conversion algor...

In this paper, we proposed the principal component analysis (PCA) fuzzy mixture model for speaker identification. A PCA fuzzy mixture model is derived from the combination of the PCA and the fuzzy version of mixture model with diagonal covariance matrices. In this method, the feature vectors are first transformed by each speaker’s PCA transformation matrix to reduce the correlation among the el...

Longitudinal data with time-dependent covariates is not readily analyzed as there are inherent, complex correlations due to the repeated measurements on the sampling unit and the feedback process between the covariates in one time period and the response in another. A generalized method of moments (GMM) logistic regression model (Lalonde, Wilson, and Yin 2014) is one method to analyze such corr...

This paper introduces and motivates the use of the statistical method Gaussian Mixture Model (GMM) and Support Vector Machines (SVM) for robust textindependent speaker identification. Features are extracted from the dialect DR1 of the Timit corpus. They are presented by MFCC, energy, Delta and Delta-Delta coefficients. GMM is used to model the feature extractor of the input speech signal and SV...

We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has been demonstrated to have computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. The key computational ...

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...

This paper introduces the performance evaluation of statististical approaches for Automatic-text-independent Speaker Recognition system. Automatic-text-independent Speaker Recognition system is to quickly and accurately identify the person from his/her voice. The study on the effect of feature vector size for good speaker recognition demonstrates that the feature vector size in the range of 18-...

We describe a simple, novel, and efficient system for speaker comparison with two main components. First, the system uses a new approximate KL divergence distance extending earlier GMM parameter vector SVM kernels. The approximate distance incorporates data-dependent mixture weights as well as the standard MAP-adapted GMM mean parameters. Second, the system applies a weighted nuisance projectio...

Speaker verification is the process of accepting or rejecting claimed identity in terms of its sound features. A speaker verification system can be used for numerous security systems, including bank account accessing, getting to security points, criminology and etc. When a speaker verification system wants to check the identity of individuals remotely, it confronts problems such as noise effect...