Sub-state tying in tied mixture hidden Markov models
An approach is proposed for partial tying of states of tied-mixture hidden Markov models. To facilitate tying at the sub-state level, the state emission probabilities are constructed in two stages, or equivalently, are viewed as a "mixture of mixtures of Gaussians." This paradigm allows, and is complemented with, an optimization technique to seek the best complexity-accuracy tradeoff solution, which jointly exploits Gaussian density sharing and sub-state tying. Experimental results on the E-set show that the classification error rate is reduced by over 20% compared to standard Gaussian sharing and whole-state tying. The approach is then embedded within the recently developed procedure of combined parameter training and reduction technique. Experiments with the overall technique show that the error rate is further reduced by 8%.