UWEE Tech Report Series

Selectively Computing Dynamic Features in the Likelihood Computation of ASR Systems


Xiao Li, Jeff Bilmes

Speech Recognition, Gaussians, Gaussian Mixtures, Power Consumption, Gaussian Selection, Speedup Computation


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 benefit in an isolated word task, it yields significant 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 performance.

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