Improvements in HMM-based isolated word recognition system

Abstract

A speaker independent isolated word recognition system for voice activated robots is introduced. Since such an application requires a fast and accurate recognition system, discrete hidden Markov models and vector quantisation techniques have been used. Different initialisations for VQ and HMM training are tested to obtain improvements over the other systems. The final models can be evaluated using a temporal normalisation of the HMM scores. A threshold based rejector can also be established using this temporal normalisation.