On the determination of epsilon during discriminative GMM training
| Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
|---|---|
| Data(s) |
27/05/2014
27/05/2014
01/12/2010
|
| 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. |
| Formato |
362-364 |
| Identificador |
http://dx.doi.org/10.1109/ISM.2010.66 Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364. http://hdl.handle.net/11449/72054 10.1109/ISM.2010.66 2-s2.0-79951728004 |
| Idioma(s) |
eng |
| Relação |
Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010 |
| Direitos |
closedAccess |
| 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 |
| Tipo |
info:eu-repo/semantics/conferencePaper |
