Model adaptation and adaptive training for the recognition of dysarthric speech

Dysarthria is a neurological speech disorder, which exhibits multi-fold disturbances in the speech production system of an individual and can have a detrimental effect on the speech output. In addition to the data sparseness problems, dysarthric speech is characterised by inconsistencies in the acoustic space making it extremely challenging to model. This paper investigates a variety of baseline speaker independent (SI) systems and its suitability for adaptation. The study also explores the usefulness of speaker adaptive training (SAT) for implicitly annihilating inter-speaker variations in a dysarthric corpus. The paper implements a hybrid MLLR-MAP based approach to adapt the SI and SAT systems. ALL the results reported uses UASPEECH dysarthric data. Our best adapted systems gave a significant absolute gain of 11.05% (20.42% relative) over the last published best result in the literature. A statistical analysis performed across various systems and its specific implementation in modelling different dysarthric severity sub-groups, showed that, SAT-adapted systems were more applicable to handle disfluencies of more severe speech and SI systems prepared from typical speech were more apt for modelling speech with low level of severity.