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.

本文中，我们研究了几种现有和一种新型的基于生成对抗网络（GAN）的语音转换方法，用于增强发音不清的语音以提高发音不清的语音识别；我们比较了现有方法的关键组件，以确定最有效的解决方案来改善发音不清的语音识别。我们发现，一些直接的信号处理方法，如固定噪声移除和基于声码器的时间拉伸，可以导致与使用最先进的 GAN 方法相当的发音不清语音识别结果；此外，我们提出的 MaskCycleGAN-VC 与时间拉伸增强的组合方案，能够改善某些发音不清的演讲者与时间拉伸基准相比，使得音素识别结果更加准确。