  In this paper, we investigate several existing and a new state-of-the-art
generative adversarial network-based (GAN) voice conversion method for
enhancing dysarthric speech for improved dysarthric speech recognition. We
compare key components of existing methods as part of a rigorous ablation study
to find the most effective solution to improve dysarthric speech recognition.
We find that straightforward signal processing methods such as stationary noise
removal and vocoder-based time stretching lead to dysarthric speech recognition
results comparable to those obtained when using state-of-the-art GAN-based
voice conversion methods as measured using a phoneme recognition task.
Additionally, our proposed solution of a combination of MaskCycleGAN-VC and
time stretched enhancement is able to improve the phoneme recognition results
for certain dysarthric speakers compared to our time stretched baseline.
