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Here, UBM is a GMM of 64 Gaussian components, estimated by using the Expectation-Maximization procedure on the training corpus.
A UBM is a GMM trained by EM parameter estimation using the training data from all the subjects in the data set.
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This reveals that the introduction of different regions s n is equivalent to assuming an affine model (18) with p(b n ) being a GMM instead of a single Gaussian density, as in (10).
The baseline system, which is based on HTK, is a triphone GMM-HMM recognizer trained on clean/multi-condition training data.
The fixed-length windows are mapped to sufficient statistics by using a universal background model (UBM) [43] which is a class-independent GMM with C Gaussians estimated with the expectation-maximization (EM) algorithm [44] on the training data set.
A GMM is a parametric probability density function represented as a weighted sum of Gaussian component densities [13, 14].
A GMM is a parametric probability density function represented as a weighted sum of M component Gaussian distributions [34], as given by the equation p ( x | λ ) = ∑ i = 1 M w i g ( x | μ i Σ i ), (3).
A GMM is a distribution function composed by adding together several 3D Gaussian density functions.
The back-end of this system is a 128-mixture GMM-based classifier that models the probability distributions of the feature values (without taking into consideration any temporal dependence).
However, System GMM is a more inefficient estimation method in our context.
The Gaussian mixture model (GMM) is a probability density function (PDF) given by a linear combination of a Gaussian PDF.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com