Maximum mutual information SPLICE transform for seen and unseen conditions

SPLICE is a front-end technique for automatic speech recognition systems. It is a non-linear feature space transformation meant to increase recognition accuracy. Our previous work has shown how to train SPLICE to perform speech feature enhancement. This paper evaluates a maximum mutual information (MMI) based discriminative training method for SPLICE. Discriminative techniques tend to excel when the training and testing data are similar, and to degrade performance significantly otherwise. This paper explores both cases in detail using the Aurora 2 corpus. The overall recognition accuracy of the MMI-SPLICE system is slightly better than the Advanced Front End standard from ETSI, and much better than previous SPLICE training algorithms. Most notably, it achieves this without explicitly resorting to the standard techniques of environment modeling, noise modeling or spectral subtraction.