On the determination of epsilon during discriminative GMM training

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.

Keywords

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