On the use of HMMs to recognize cerebral palsy speech: isolated word case

Abstract

A conventional HMM (hidden Markov model) method has been applied to the problem of isolated-word cerebral palsy speech recognition. A full-structure HMM was found to provide best results because of the conditions of high variability and small amounts of training data. To overcome the inadequacies of the conventional method, an enhanced clipping procedure has been developed which aids in the removal of variability in both the training and recognition phases of the HMM procedure. The performance of isolated-word recognition was significantly improved when this enhanced procedure was applied in a case study. >