DISCRIMINATIVE TRAINING BASED ON MINIMUM CLASSIFICATION ERROR FOR A SMALL AMOUNT OF DATA ENHANCED BY VECTOR-FIELD-SMOOTHED BAYESIAN LEARNING

This paper describes how to effectively use discriminative training ba sed on Minimum Classification Error (ME) criterion for a small amount of data in order to attain the highest Level of recognition performanc e. This method is a combination of MCE training and Vector-Field-Smoot hed Bayesian learning called MAP/VFS, which combines maximum a posteri ori (MAP) estimation with Vector Field Smoothing (VFS). In the propose d method, MAP/VFS can significantly enhance MCF training in the robust ness of acoustic modeling. In model training, MCE training is performe d using the MAP/VFS-trained model as an initial model. The same data a re used in both trainings. For speaker adaptation using several dozen training words, the proposed method has been experimentally proven to be very effective. For 50-word training data, recognition errors are d rastically reduced by 47% compared with 16.5% when using only MCE. Thi s high rate, in which 39% is due to MAP, an additional 4% is due to VF S, and a further improvement of 4% is due to MCE, can be attained by e nhancing MCE training capability by MAP/VFS.