This paper presents adaptive voice-quality control methods based on one-to-many eigenvoice conversion. To intuitively control the converted voice quality by manipulating a small number of control parameters, a multiple regression Gaussian mixture model (MR-GMM) has been proposed. The MR-GMM also allows us to estimate the optimum control parameters if target speech samples are available. However...

Weak instruments arise when the instruments in linear IV regression are weakly correlated with the included endogenous variables. In nonlinear GMM, weak instruments correspond to weak identification of some or all of the unknown parameters. Weak identification leads to non-normal distributions, even in large samples, so that conventional IV or GMM inferences are misleading. Fortunately, various...

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions based on multiplicative or additive errors; within groups fixed effects Poisson for panel data; GMM est...

A novel idea of using giant magnetostrictive material (GMM) based actuators for journal bearing control is presented in this paper. Frequency response tests on GMM actuators and a journal bearing system were examined. The performances of the system running at various journal shaft rotational speeds under control were investigated. With the aid of GMM actuators, the performances on journal shaft...

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...

This paper presents a novel statistical sample-based approach for Gaussian Mixture Model (GMM)-based Voice Conversion (VC). Although GMM-based VC has the promising flexibility of model adaptation, quality in converted speech is significantly worse than that of natural speech. This paper addresses the problem of inaccurate modeling, which is one of the main reasons causing the quality degradatio...

While the classical framework has a rich set of limited information procedures such as GMM and other related methods, the situation is not so in the Bayesian framework. We develop a limited information procedure in the Bayesian framework that does not require the knowledge of the full likelihood. The developed procedure is a Bayesian counterpart of the classical GMM but has advantages over the ...

Gaussian mixture modeling with universal background model (GMM-UBM) is a widely used method for speaker identification, where the GMM model is used to characterize a specific speaker’s voice. The estimation of model parameters is generally performed based on the maximum likelihood (ML) or maximum a posteriori (MAP) criteria. In this way, interspeaker information that discriminates between diffe...

The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Model) is a finite mixture probability distribution model. Although the two models have a close relationship, they are always discussed independently and separately. The EM (Expectation-Maximum) algorithm is a general me...

The current paper proposes skew Gaussian mixture models for speaker recognition and an associated algorithm for its training from experimental data. Speaker identification experiments were conducted, in which speakers were modeled using the familiar Gaussian mixture models (GMM), and the new skewGMM. Each model type was evaluated using two sets of feature vectors, the mel-frequency cepstral coe...