On the determination of epsilon during discriminative GMM training
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2010-12-01
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Coorientador
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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
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Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364.