Improved discriminative training techniques for large vocabulary continuous speech recognition

Investigates the use of discriminative training techniques for large vocabulary speech recognition with training datasets up to 265 hours. Techniques for improving lattice-based maximum mutual information estimation (MMIE) training are described and compared to frame discrimination (FD). An objective function which is an interpolation of MMIE and standard maximum likelihood estimation (MLE) is also discussed. Experimental results on both the Switchboard and North American Business News tasks show that MMIE training can yield significant performance improvements over standard MLE even for the most complex speech recognition problems with very large training sets