Empirical researchers frequently use dynamic panel data models and employ Generalized Method of Moments (GMM) estimators (Hansen, 1982) to estimate model parameters. An important practical problem in the estimation of a dynamic panel data model is the choice of moments as it provides a large number of moment conditions. Even though adding moment conditions leads to efficiency gain according to ...

We are using Gaussian Mixture Models (GMM) as a tool to construct local mappings of nonlinear Multi-Input Multi-Output (MIMO) systems. In this work we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with Vector Quantization (VQ) or its more parsi...

This paper describes a feature extraction technique based on fitting a Gaussian mixture model (GMM) to the speech spectral envelope. The features obtained (the component means, variances and priors) represent both the the general shape of the spectrum and provide information on the position of the spectral peaks. As the features select peaks in the spectrum they are related to the formant ampli...

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...

Despite possible structural changes related to atrophy and edema, the structural anatomy of the brain should present time consistency for a given patient. Based on this assumption, we propose a lesion segmentation method that first derives a gaussian mixture model (GMM) separating healthy tissues from pathological and unexpected ones on a multi-time-point intra-subject groupwise image. This ave...

The aim of this section is to develop a high-performance semantic indexing system using Gaussian mixture model (GMM) supervectors and tree-structured GMMs [1, 2]. GMM spervectors corresponding to six types of audio and visual features are extracted from video shots by using tree-structured GMMs. The computational cost of maximum a posteriori (MAP) adaptation for estimating GMM parameters are re...

In this paper, a GMM with common mixture components, referred to as the common component GMM (CCGMM), is proposed to be the signal model for calculating the diversity measure for the speaker-and-environment change detection in broadcast news signal. The use of GMM is to increase the accuracy of audio signal modeling while the use of common mixture components is to solve the complexity problem o...

The paper considers estimation and inference of time series GMM models where a subset of parameters are time varying. The magnitude of the time variation in the unstable parameters is such that efficient tests detect the instability with (possibly high) probability smaller than one, even in the limit. We show that for many forms of parameter instability and for a large class of GMM models, stan...

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...

The present work demonstrates experimental evaluation of speaker verification for different speech feature extraction techniques with the constraints of limited data (less than 15 seconds). The state-of-the-art speaker verification techniques provide good performance for sufficient data (greater than 1 minutes). It is a challenging task to develop techniques which perform well for speaker verif...