This paper proposes a new mapping method combining GMM and codebook mapping methods to transform spectral envelope for voice conversion system. After analyzing overly smoothing problem of GMM mapping method in detail, we propose to convert the basic spectral envelope by GMM method and convert envelope-subtracted spectral details by GMM and phone-tied codebook mapping method. Objective evaluatio...

This paper analyzes the higher order asymptotic properties of Generalized Method of Moments (GMM) estimators for linear time series models using many lags as instruments. A data dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel weighted moment conditions is proposed. It is shown ...

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to th...

A maximum likelihood parameter trajectory estimation based on a Gaussian mixture model (GMM) has been successfully implemented for acoustic-to-articulatory inversion mapping. In the conventional method, GMM parameters are optimized by maximizing a likelihood function for joint static and dynamic features of acoustic-articulatory data, and then, the articulatory parameter trajectories are estima...

The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn’t require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the ...

This entry describes the basic framework for statistical estimation and inference using Generalized Method of Moments and also illustrates the types of empirical models in finance to which these techniques have been applied. GeneralizedMethod of Moments (GMM) provides a computationally convenientmethod of obtaining consistent and asymptotically normally distributed estimators of the parameters ...

The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing wei...

In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Mo...

The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. For pattern classification, however, the GMM has to consider two issues: model structure in high-dimensional space and discriminative training for optimizing the decision boundary. In this paper, we propose a classification method using subspace GMM density mo...

Point set registration is to determine correspondences between two different point sets, then recover the spatial transformation between them. Many current methods, become extremely slow as the cardinality of the point set increases; making them impractical for large point sets. In this paper, we propose a bi-stage method called bi-GMMTPS, based on Gaussian Mixture Models and Thin-Plate Splines...