Efficient mixture Gaussian synthesis for decision tree based state tying

We propose an efficient mixture Gaussian synthesis method for decision tree based state tying that produces better context-dependent models in a short period of training time. This method makes it possible to handle mixture Gaussian HMMs in the decision tree based state tying algorithm, and provides a higher recognition performance compared to the conventional HMM training procedure using decision tree based state tying on single Gaussian HMMs. This method also reduces the steps of the HMM training procedure because the mixture incrementing process is not necessary. We applied this method to the training of telephone speech triphones, and evaluated its effect on Japanese phonetically balanced sentence tasks. Our method achieved a 1 to 2 point improvement in phoneme accuracy and a 67% reduction in training time