Discriminative training of Gaussian mixture models for large vocabulary speech recognition systems

Two discriminative techniques are described (and evaluated) for estimating the parameters of the Gaussians in a large vocabulary speech-recognition system. The first technique is based on using a modification of the MMI objective function, and appears to provide no improvement over standard ML estimation. The second technique is based on a heuristic correction of the Gaussian parameters, and is seen to give a 2-5% improvement over ML estimation.