Sequential clustering algorithm for Gaussian mixture initialization

A simple sequential algorithm for deriving initial values for Gaussian mixture parameters used in HMM-based speech recognition is presented. The proposed algorithm sequentially clusters the training frames, in the order in which they are available and according to the density to which they are associated. This frame-density association results from a frame-state alignment of the training data performed with a single-Gaussian model, which is good enough for such a force-alignment task. The models obtained with the proposed sequential clustering procedure provide good speech recognition performance when compared to models obtained with the usual Gaussian splitting procedure.