Publicação: On the determination of epsilon during discriminative GMM training
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
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.
Descrição
Palavras-chave
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
Idioma
Inglês
Como citar
Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364.
