Context-Dependent Word Duration Modeling for Korean Connected Digit Recognition

Abstract

In a Korean connected digit recogniser, duration modelling is necessary to reduce insertion and deletion errors due to monophonemic digits, which cannot usually be corrected even by discriminative training algorithms. In the Latter the authors incorporate context-dependent word duration modelling directly in a decoding algorithm to reduce those errors. While incorporating duration information in the postprocessing stage shows little improvements over a baseline system, the proposed method reduces word error rates by similar to 10% for unknown length decoding when both maximum likelihood estimation and generalised probabilistic descent training are used.