Comparison and study of some variants of partially tied covariance modeling

Some practical implementation issues on partially tied covariance (PTC) modeling are discussed. First, from the view of model complexity and computational load, a comparison is made for some variants of PTC. From the analysis, two representatives, STC and Ortho-STC are compared in detail. Second, based on these variants, two techniques are studied. One technique is joint optimization of both transformation and HMM parameters, which will exploit the potential of PTC. The other technique is model selection by hierarchical tree via Bayesian information criterion (BIC), which will decide the number and structure of transformation classes thus to assure the generalization capacity. Experiment results showed that STC always outperforms Ortho-STC due to the effect of parameter tying and by the application of above two techniques the system performance can be much improved.