Title :
Maximum likelihood clustering of Gaussians for speech recognition
Author :
Kannan, A. ; Ostendorf, M. ; Rohlicek, J.R.
Author_Institution :
Dept. of Electr. Comput. & Syst. Eng., Boston Univ., MA, USA
fDate :
7/1/1994 12:00:00 AM
Abstract :
Describes a method for clustering multivariate Gaussian distributions using a maximum likelihood criterion. The authors point out possible applications of model clustering, and then use the approach to determine classes of shared covariances for contest modeling in speech recognition, achieving an order of magnitude reduction in the number of covariance parameters, with no loss in recognition performance
Keywords :
maximum likelihood estimation; speech recognition; stochastic processes; contest modeling; covariance parameters; maximum likelihood clustering; maximum likelihood criterion; model clustering; multivariate Gaussian distributions; recognition performance; shared covariances; speech recognition; Context modeling; Distributed computing; Gaussian distribution; Gaussian processes; Hidden Markov models; Maximum likelihood estimation; Parameter estimation; Performance loss; Robustness; Speech recognition;
Journal_Title :
Speech and Audio Processing, IEEE Transactions on
