A dynamical system approach to continuous speech recognition

An dynamical system model is proposed for better representing the spectral dynamics of speech for recognition. It is assumed that the observed feature vectors of a phone segment are the output of a stochastic linear dynamical system, and two alternative assumptions regarding the relationship of the segment length and the evolution of the dynamics are considered. Training is equivalent to the identification of a stochastic linear system, and a nontraditional approach based on the estimate-maximize algorithm is followed. This model is evaluated on a phoneme classification task using the TIMIT database. It is shown that the classification performance obtained using the proposed model is significantly better than that obtained using either an independent-frame or a Gauss-Markov assumption on the observed frames