State mixture modelling applied to speech and speaker recognition

In state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculations from 2N TT (HMM direct calculation) and N 2T (Forward-backward algorithm) to just only 2NT calculations, but also achieves a better recognition result.