Covariance function for the Speaker-centred data
Nonparametric Covariance Function Estimation for Functional and Longitudinal Data
... the covariance function as well as the functional principal components for both subsets and the results are given in the middle and left panels of Figure 4 for comparison ...the covariance ...
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Dataset-invariant covariance normalization for out-domain PLDA speaker verification
... domain-invariant covariance normalization (DICN) technique to relocate both in-domain and out-domain i-vectors into a third dataset-invariant space, pro- viding an improvement for out-domain PLDA speaker ...
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Non-parametric and semi-parametric estimation of spatial covariance function
... Their covariance function depends on longitude only through their ...ozone data described above on a global scale, where they consider the axially symmetric process by applying differential operators ...
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Duration mismatch compensation using four-covariance model and deep neural network for speaker verification
... We explored two approaches for compensating duration mismatch in i-vector based speaker recognition systems. The first one adapts PLDA modeling to the specific case of mis- matched data ...
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Limited-data automatic speaker verification algorithm using band-limited phase-only correlation function
... automatic speaker verification based on band-limited phase- only correlation (BLPOC) is ...BLPOC function as a new limited-data automatic speaker verification ...some speaker ...
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Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator
... and covariance matrix obtained from a ( p+1) subset of observations, which is iterated towards better approximations using Newton-steps (for S) or the so-called C-steps which are used in the FAST-MCD ...normal ...
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Asymptotic Behavior of the Likelihood Function of Covariance Matrices of Spatial Gaussian Processes
... sample data is considered as fixed. An arbitrary feasible covariance model function is chosen for good, so that only the covariance models’ hyperparameters are allowed to vary in order to ...
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Joint Mean and Covariance Modeling of Matrix-Variate Data
... the data, the pitch curves primarily occupy a subset of the Euclidean space R 19 in which the pitch curve vectors ...raw data tend to have very large condition ...given speaker uttering a given ...
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Covariance-based Clustering in Multivariate and Functional Data Analysis
... objective function was highest. Since the number of data in each sub-population, K, is high with respect to their dimensionality, P = 2, we used Max-Swap algorithm in combination with the standard sample ...
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Optimal Nonparametric Covariance Function Estimation for Any Family of Nonstationary Random Processes
... Our data consist of n = 170 HRV measurements with sampling rate 2 ...the data has been removed, we consider it to be an observation of a nonstationary zero-meaned random ...the covariance ...
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Modeling and Prediction of Network Traffic Based on Hybrid Covariance Function Gaussian Regressive
... MAWI data, actual network data taken from Japan network to USA network, are selected as simulation ...hybrid covariance function GP to analyze ...
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Joint Variable Selection of Mean Covariance Model for Longitudinal Data
... 1 , , i T i i im m i 1 vector and i The rest of this paper is organized as follows. In Sec- tion 2 we first describe a reparameterization of covari- ance matrix itself through the modified Cholesky de- composition ...
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Forecast sensitivity to the observation error covariance in variational data assimilation
... of data in reducing the forecast errors. The observation error covariance sensitivity analysis is particularly valuable for satellite data where accurate estimations of the observational errors ...
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Data Model Relationship in Text Independent Speaker Recognition
... of data, particu- larly training ...a function of training data is now consid- ...three speaker recognition systems which differ only in the quantity of data that is used for model ...of ...
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Analysis error covariance versus posterior covariance in variational data assimilation
... error covariance and Bayesian posterior covariance can differ quite ...error covariance should probably be considered in relation to the confidence intervals/regions issue, and the Bayesian posterior ...
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Speaker Identification Based Speaker Segmentation for Meeting Data
... 3. Feature Extraction- The first step in any automatic speaker recognition system is to extract features i.e. identify the components of the audio signal that are good for identifying the linguistic content and ...
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Covariance symmetries detection in PolInSAR data
... A. Proposed Framework Fig. 1 summarizes the overall pipeline of the proposed framework as four main steps. The input consists of a PolIn- SAR cross-covariance matrix data set (i.e., C 12 or C 21 ). As the ...
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Covariance symmetries detection in PolInSAR data
... In the following, we use them separately. A. Proposed Framework Fig. 1 summarizes the overall pipeline of the proposed framework as four main steps. The input consists of a PolIn- SAR cross-covariance matrix ...
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Covariance Estimation with Missing and Dependent Data
... 1.2 Covariance Estimation for Matrix-Variate Data with Missing Values and Mean Structure Another data complication of particular interest is adding dependence between the obser- ...the data ...
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The Effects of Covariance Structures on Modelling of Longitudinal Data
... Fixed covariance structures of random error and random effect are assumed in linear mixed ...real data, we cannot make correct statistical ...about covariance structures and comes over the above ...
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