This paper first introduces the theory of Stochastic Trajectory Models (STMs). STM represents the acoustic observations of a speech unit as clusters of trajectories in a parameter space. The trajectories are modeled by mixture of probability density functions of random sequence of states. Each state is associated with a multi-variate Gaussian density function, optimized at state sequence level. The effect of not using the HMM assumptions in STM is that STM can exploit information, such as time correlation within an observation sequence, which is hidden by HMM assumptions.
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