This paper introduces a new statistical framework for constructing triphonic models from models of less context-dependency. This composition reduces the number of models to be estimated by higher than an order of magnitude and is therefore of great significance in relieving the data sparsity problem in triphone-based continuous speech recognition. The new framework is derived from Bayesian statistics, and represents an alternative to other triphone-by-composition techniques, particularly to the model-interpolation and quasitriphone approaches. The potential power of this new framework is explored by an implementation based on the hidden Markov modeling technique. It is shown that the new model structure includes the quasitriphone model as a special case, and leads to more efficient parameter estimation than the model-interpolation method. Phone recognition experiments show an increase in the accuracy over that obtained by comparable models.

Two methods of improving conventional n-gram statistical language models are examined. The first involves using a new set of n-gram statistics that attempt to improve the ability of a system to identify phrases correctly. The second involves replacing the maximum likelihood unigram component with an optimised distribution. We test these approaches by incorporating them into weighted average [1] and deleted estimate [2] language models trained on a large newspaper corpus. The improvements lead to a reduction in perplexity of 4.5% and 4.9% respectively for these models.

An optimized hidden Markov model (HMM) with two kinds of inter-frame dependent observation structures, both built on the observation densities of a first-order dependent form, is presented to account for the statistical dependence between successive frames. In the first model, the dependence relation among the frames is determined optimally by maximizing the likelihood of the observations in both training and testing. In the second model, the dependence structure associated with each frame is described by a weighted sum of the conditional densities of the frame given individual previous frames. The segmental K-means and the forward-backward algorithms are implemented, respectively, for the estimation of the parameters of the two models. Experimental comparisons for an isolated word recognition task show that these models achieve better performance than both the standard continuous HMM and the bigram-constrained HMM.