Rusheng Hu, Xiaolong Li, and Yunxin Zhao
22 March 2005
In this paper, we present a greedy EM (GEM) method for
training Gaussian mixture density (GMD) based acoustic models.
In the proposed approach, starting from a single Gaussian, GMD
is built up by sequentially adding new components. Each new
component is globally selected to avoid local optima. The
sequential procedure offers more control over the model
structure to achieve better coverage of data. GEM also provides a natural way of integrating information criterion for model complexity selection. Experimental results on WSJ task show that the new method performs consistently better than the conventional method in speech recognition word error rate.
In Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP'05)
Publisher Institute of Electrical and Electronics Engineers, Inc.
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