In [1], we described how to improve Semi-Continuous Density Hidden Markov Models (SC-HMMs) to be as fast as Continuous Density HMMs (CD-HMMs), whilst outperforming them on large vocabulary recognition tasks with context independent models. In this paper, we extend our work with SC-HMMs to context dependent modelling. We propose a novel node splitting criterion in an approach with phonetic decision trees. It is based on a distance measure between mixture gaussian probability density functions (pdfs) as used in the final tied state SC-HMMs, this in contrast with other criteria which are based on simplified pdfs to manage the algorithm complexity. Results on the ARPA Resource Management task show that the proposed criterion outperforms two of these criteria with simplified pdfs.