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Minimum error rate training for designing tree-structured probability density function

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1 Author(s)
Wu Chou ; AT&T Bell Labs., Murray Hill, NJ, USA

We propose a signal prototype classification and evaluation framework in acoustic modeling. Based on this framework, a new tree-structured likelihood function is derived. It uses a designated cluster kernel fmC for signal prototype classification and a designated cluster kernel fmL for likelihood evaluation of outlier or tail events of the cluster. A minimum classification error (MCE) rate training approach is described for designing tree-structured likelihood function. Experimental results indicate that the new tree-structured likelihood function significantly improves the acoustic resolution of the model. It has a more significant speedup in decoding than the one obtained from the conventional approach

Published in:

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on  (Volume:2 )

Date of Conference:

21-24 Apr 1997