  Cleft lip and palate (CLP) refer to a congenital craniofacial condition that
causes various speech-related disorders. As a result of structural and
functional deformities, the affected subjects' speech intelligibility is
significantly degraded, limiting the accessibility and usability of
speech-controlled devices. Towards addressing this problem, it is desirable to
improve the CLP speech intelligibility. Moreover, it would be useful during
speech therapy. In this study, the cycle-consistent adversarial network
(CycleGAN) method is exploited for improving CLP speech intelligibility. The
model is trained on native Kannada-speaking childrens' speech data. The
effectiveness of the proposed approach is also measured using automatic speech
recognition performance. Further, subjective evaluation is performed, and those
results also confirm the intelligibility improvement in the enhanced speech
over the original.
