Temporal decomposition for the initialization of a HMM isolated word-recognizer

The technique of temporal decomposition is used to initialize continuous density hidden Markov models. The temporal decomposition process produces a representation of each word in terms of a set of target vectors and interpolation functions. Roughly speaking, the target vectors represent the centers of the important acoustic events, and the interpolation functions describe a spectral path between these events. The number of targets generated by the temporal decomposition process is taken to be the number of states used for the HMM, and the position, shape and length of the interpolation functions are used to provide initial estimates for the transition probabilities and observation probability densities of the HMM. The performance of such a system is assessed for a single-speaker environment