GMM
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This document discusses Gaussian mixture models (GMMs) and their use in applications like speaker recognition and language identification. GMMs represent a probability density function as a weighted sum of Gaussian distributions. GMM parameters are estimated from training data using Expectation-Maximization or Maximum A Posteriori estimation. GMMs are computationally inexpensive and well-suited for text-independent tasks without strong prior knowledge of content.
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2nd annual io t summit 2016 - Bangalore India
Greetings from Virtue Insight,
I am happy to invite you and your colleagues to be a sponsor/ delegate for our upcoming “2nd Annual IoT Summit 2016” The conference will Be held on 27th July 2016, The Lalit Ashok Hotel, Bangalore, India.
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Delegate and Sponsorship Sales
Virtue Insight
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Digital digest //24.06.2016
Собираем самые актуальные новости из digital каждые две недели. Будь в курсе вместе с нами! // The news over the past two weeks from digital world. Stay informed along with us
8 3 giraldo anderson y jhon trejos
El documento habla sobre las sustancias psicoactivas y los problemas que ocasionan. Explica que las drogas causan adicciones y demencias, afectando negativamente la vida de quienes las consumen. Además, menciona las diferentes clases de drogas y los problemas ocasionados por su consumo y combinaciones.
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- 1. A Gaussian Mixture Model (GMM) is a parametric representation of a probability density function, based on a weighted sum of multi-variate Gaussian distributions GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model
- 2. GMM is computationally inexpensive, does not require phonetically labeled training speech, and is well suited for text-independent tasks, where there is no strong prior knowledge of the spoken text. GMM is used in Signal Processing , Speaker Recognition, Language Identification, Classification etc Big phoneme or vocabulary database no needed. Text independent verification. HMM doesn’t shown advantage over GMM.
- 3. An Expectation–Maximization (EM) algorithm is an iterative method for finding Maximum Likelihood or Maximum A Posteriori(MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables EM Algorithm consists of two major steps: 1st --E (Expectation) step 2nd--M (Maximization) step
- 4. E (Expectation) step: In the E-step, the expected value of the log-likelihood function is calculated given the observed data and current estimate of the model parameters. M (Maximization) step: The M-step computes the parameters which maximize the expected log-likelihood found on the E-step. These parameters are then used to determine the distribution of the latent variables in the next E-step until the algorithm has converged.
- 6. A maximum a posteriori probability (MAP) estimate is a mode of the posterior distribution The MAP estimation is a two step estimation process: 1st-- Estimates of the sufficient statistics of the training data are computed for each mixture in the prior model. 2nd-- For adaptation these ‘new’ sufficient statistic estimates are then combined with the ‘old’ sufficient statistics from the prior mixture parameters using a data-dependent mixing coefficient.
- 7. In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters
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