High-accuracy large-vocabulary speech recognition using mixture tying and consistency modeling

Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose ascheme that optimizes the degree of mixture tying for a given amount of training data and computational resources. Experimental results on the Wall Street Journal (WSJ) Corpus show that this new form of output distri-bution achieves a 25 % reduction in error rate over typical tied-mixture systems. We then show that an additional improvement can be achieved by modeling local time correlation with linear discriminant features. 1