In this paper we present a new approach for a generalized tying of mixture components for continuous mixture-density HMM-based speech recognition systems. With an iterative pruning and splitting procedure for the mixture components, this approach offers a very accurate and detailed representation of the acoustic space and at the same time keeps the number of parameters reasonably small in favor of a robust parameter estimation and a fast decoding. Contrary to other approaches, it does not require a strict clustering of the pdfs into subsets that share their mixture components, so that it is capable of providing more general and flexible types of mixture tying. We applied the new approach on a semi-continuous HMM (SCHMM)- system for the Resource Management task and improved its recognition performance by 12% and vastly accelerated the decoding because of a much faster likelihood computation.