On the determination of epsilon during discriminative GMM training
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Data
2010-12-01
Orientador
Coorientador
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Resumo
Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.
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Discriminative training of Gaussian Mixture Models (GMMs), Markov Models, Speaker identification, Speech recognition, Discriminative training, Gaussian mixture models, Gradient descent algorithms, Gradient Descent method, Iteration step, Newton-Raphson iterative method, Second orders, Speaker recognition, Gaussian distribution, Iterative methods, Loudspeakers, Markov processes
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Inglês
Como citar
Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364.
