  Disordered speech recognition is a highly challenging task. The underlying
neuro-motor conditions of people with speech disorders, often compounded with
co-occurring physical disabilities, lead to the difficulty in collecting large
quantities of speech required for system development. This paper investigates a
set of data augmentation techniques for disordered speech recognition,
including vocal tract length perturbation (VTLP), tempo perturbation and speed
perturbation. Both normal and disordered speech were exploited in the
augmentation process. Variability among impaired speakers in both the original
and augmented data was modeled using learning hidden unit contributions (LHUC)
based speaker adaptive training. The final speaker adapted system constructed
using the UASpeech corpus and the best augmentation approach based on speed
perturbation produced up to 2.92% absolute (9.3% relative) word error rate
(WER) reduction over the baseline system without data augmentation, and gave an
overall WER of 26.37% on the test set containing 16 dysarthric speakers.
