  The automatic recognition of pathological speech, particularly from children
with any articulatory impairment, is a challenging task due to various reasons.
The lack of available domain specific data is one such obstacle that hinders
its usage for different speech-based applications targeting pathological
speakers. In line with the challenge, in this work, we investigate a few data
augmentation techniques to simulate training data for improving the children
speech recognition considering the case of cleft lip and palate (CLP) speech.
The augmentation techniques explored in this study, include vocal tract length
perturbation (VTLP), reverberation, speaking rate, pitch modification, and
speech feature modification using cycle consistent adversarial networks
(CycleGAN). Our study finds that the data augmentation methods significantly
improve the CLP speech recognition performance, which is more evident when we
used feature modification using CycleGAN, VTLP and reverberation based methods.
More specifically, the results from this study show that our systems produce an
improved phone error rate compared to the systems without data augmentation.
