Acoustic Modeling Using Greedy EM

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.

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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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