Automatic speech recognition with sparse training data for dysarthric speakers

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.