A Comparative Study on Maximum Entropy and Discriminative Training for Acoustic Modeling in Automatic Speech Recognition

While Maximum Entropy (ME) based learning procedures have been successfully applied to text based natural language processing, there are only little investigations on using ME for acoustic modeling in automatic speech recognition. In this paper we show that the well known Generalized Iterative Scaling (GIS) algorithm can be used as an alternative method to discriminatively train the parameters of a speech recognizer that is based on Gaussian densities. The approach is compared with both a conventional maximum likelihood training and a discriminative training based on the Extended Baum algorithm. Experimental results are reported on a connected digit string recognition task