Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to...

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...

This study examines the impacts of real exchange rate fluctuations on the companies' strategic investments in Iran. The data of 92 listed companies in Tehran Stock Exchange during the period of 2002-2015areused. First, the volatility of exchange rate is estimated by the Generalized Autoregressive Conditional Heteroskedasticity (GARCH). The model is estimated by GMM and system GMM methods. The r...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components in speech decoding. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performance ...

In earlier studies, we employed a large prosodic feature vector to assess the quality of L2 learner’s utterances with respect to sentence melody and rhythm. In this paper, we combine these features with two standard approaches in paralinguistic analysis: (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), and (2) openSMILE, an open-source toolkit for...

A spectral conversion method using multiple Gaussian Mixture Models (GMMs) based on the Bayesian framework is proposed. A typical spectral conversion framework is based on a GMM. However, in this conventional method, a GMM-appropriate number of mixtures is dependent on the amount of training data, and thus the number of mixtures should be determined beforehand. In the proposed method, the varia...

Gaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components of speech recognition. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performan...

Methods for missing-feature reconstruction substitute noisecorrupted features with clean-speech estimates calculated based on reliable information found in the noisy speech signal. Gaussian mixture model (GMM) based reconstruction has conventionally focussed on reliable information present in a single frame. In this work, GMM-based reconstruction is applied on windows that span several time fra...

This paper presents a comparison between Support Vector Machines (SVM) speaker verification systems based on linear and non linear kernels defined in GMM supervector space. We describe how these kernel functions are related and we show how the nuisance attribute projection (NAP) technique can be used with both of these kernels to deal with the session variability problem. We demonstrate the imp...