GAUSSIAN MIXTURE MODELING WITH VOLUME PRESERVING NONLINEAR FEATURE SPACE TRANSFORMS

Peder A. Olsen,Scott Axelrod,Karthik Visweswariah,Ramesh A. Gopinath

ieee automatic speech recognition and understanding workshop（2003）

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摘要

ABSTRACT This paper introduces a new class of nonlinear feature space transformations in the context of Gaussian Mixture Models. This class of nonlinear transformations is characterized by computationally efficient training algorithms. Experimental results with quadratic feature space transforms are shown to yield modestly improved recognition performance in a speech recognition context. The quadratic feature space transforms are also shown,to be beneficial in an adaptation setting.

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关键词

Gaussian processes,learning (artificial intelligence),speech recognition,transforms,Gaussian mixture models,nonlinear feature space transforms,quadratic feature space transforms,speech recognition,training algorithms

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