Minimum classification error training for a small amount of data enhanced by vector-field-smoothed Bayesian learning
Junichi Takahashi, Shigeki Sagayama · 2002
This paper describes an efficient method of attaining the highest level of recognition performance yet achieved in minimum classification error (MCE) training for a small amount of data. This method combines MCE and vector-field-smoothed Bayesian learning called MAP/VFS. In the proposed method, the training capability of MCE in robust acoustic modeling can be significantly enhanced with MAP/VFS. In the method, MCE training is performed using an initial model trained through MAP/VFS. The same data are used in both training. For speaker adaptation using 50-word training data, the error reduction rate drastically rises to 47% compared with 16.5% when using only MCE. This high rate, in which 39% is due to MAP, an additional 4% is due to VFS, and a further improvement of 4% is due to MCE, can be attained by enhancing MCE training capability with MAP/VFS.