Selectively Computing Dynamic Features in the Likelihood Computation of ASR Systems
This paper proposes a novel technique to reduce the likelihood computation in ASR systems that use continuous density HMMs. Based on the nature of dynamic features and the numerical properties of Gaussian mixture distributions, we approximate the observation likelihood computation to achieve a speedup. Although the technique does not show appreciable benet in an isolated word task, it yields signicant improvements in continuous speech recognition. For example, 50% of the computation can be saved on the TIMIT database with only a negligible degradation in system performanc