Minimum error rate training for designing tree-structured probability density function

We propose a signal prototype classification and evaluation framework in acoustic modeling. Based on this framework, a new tree-structured likelihood function is derived. It uses a designated cluster kernel f_m^C for signal prototype classification and a designated cluster kernel f_m^L for likelihood evaluation of outlier or tail events of the cluster. A minimum classification error (MCE) rate training approach is described for designing tree-structured likelihood function. Experimental results indicate that the new tree-structured likelihood function significantly improves the acoustic resolution of the model. It has a more significant speedup in decoding than the one obtained from the conventional approach