State constrained dynamic programming (SCDP) for discrete utterance recognition

Abstract

A new dynamic programming (DP) formulation, Using the state characteristics of the data to constrain registration, is introduced. It is evaluated in comparison to a standard DP formulation using ten untrained male and female talkers with two separate ten utterance vocabularies. One of these vocabularies was specifically designed to assess the strengths and weaknesses of the analysis system and DP formulations. Results show that the new method is clearly superior to the standard formulation with regard to memory requirements and computational load. While recognition accuracy is only slightly better for one version of the new method, the character of the error is more "sensible" and explainable. This factor is thought to be important for reject control in a discrete utterance recognition system. The parameterization of the new system also provides a better way to marry system specification with system performance.