Guido, Rodrigo Capobianco [UNESP]Chen, Shi-Huang [UNESP]Junior, Sylvio Barbon [UNESP]Souza, Leonardo Mendes [UNESP]Vieira, Lucimar Sasso [UNESP]Rodrigues, Luciene Cavalcanti [UNESP]Escola, Joao Paulo Lemos [UNESP]Zulato, Paulo Ricardo Franchi [UNESP]Lacerda, Michel Alves [UNESP]Ribeiro, Jussara [UNESP]2014-05-272014-05-272010-12-01Proceedings - 2010 IEEE International Symposium on Multimedia, ISM 2010, p. 362-364.http://hdl.handle.net/11449/72054Discriminative 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.362-364engDiscriminative training of Gaussian Mixture Models (GMMs)Markov ModelsSpeaker identificationSpeech recognitionDiscriminative trainingGaussian mixture modelsGradient descent algorithmsGradient Descent methodIteration stepNewton-Raphson iterative methodSecond ordersSpeaker recognitionGaussian distributionIterative methodsLoudspeakersMarkov processesTrabalho apresentado em evento10.1109/ISM.2010.66Acesso aberto2-s2.0-7995172800465420862268080670000-0002-0924-8024