  Dysarthric speech reconstruction (DSR) systems aim to automatically convert
dysarthric speech into normal-sounding speech. The technology eases
communication with speakers affected by the neuromotor disorder and enhances
their social inclusion. NED-based (Neural Encoder-Decoder) systems have
significantly improved the intelligibility of the reconstructed speech as
compared with GAN-based (Generative Adversarial Network) approaches, but the
approach is still limited by training inefficiency caused by the cascaded
pipeline and auxiliary tasks of the content encoder, which may in turn affect
the quality of reconstruction. Inspired by self-supervised speech
representation learning and discrete speech units, we propose a Unit-DSR
system, which harnesses the powerful domain-adaptation capacity of HuBERT for
training efficiency improvement and utilizes speech units to constrain the
dysarthric content restoration in a discrete linguistic space. Compared with
NED approaches, the Unit-DSR system only consists of a speech unit normalizer
and a Unit HiFi-GAN vocoder, which is considerably simpler without cascaded
sub-modules or auxiliary tasks. Results on the UASpeech corpus indicate that
Unit-DSR outperforms competitive baselines in terms of content restoration,
reaching a 28.2% relative average word error rate reduction when compared to
original dysarthric speech, and shows robustness against speed perturbation and
noise.
