Automatic speech recognition with sparse training data for dysarthric speakers

Abstract

We describe an unusual ASR application: recognition of command words from severely dysarthric speakers, who have poor control of their articulators.The goal is to allow these clients to control assistive technology by voice.While this is a small vocabulary, speaker-dependent, isolated-word application, the speech material is more variable than normal, and only a small amount of data is available for training.After training a CDHMM recogniser, it is necessary to predict its likely performance without using an independent test set,so that confusable words can be replaced by alternatives.We present a battery of measures of consistency and confusability, based on forced-alignment, which can be used to predict recogniser performance.We show how these measures perform, and how they are presented to the clinicians who are the users of the system.