  Automatic recognition of dysarthric speech remains a highly challenging task
to date. Neuro-motor conditions and co-occurring physical disabilities create
difficulty in large-scale data collection for ASR system development. Adapting
SSL pre-trained ASR models to limited dysarthric speech via data-intensive
parameter fine-tuning leads to poor generalization. To this end, this paper
presents an extensive comparative study of various data augmentation approaches
to improve the robustness of pre-trained ASR model fine-tuning to dysarthric
speech. These include: a) conventional speaker-independent perturbation of
impaired speech; b) speaker-dependent speed perturbation, or GAN-based
adversarial perturbation of normal, control speech based on their time
alignment against parallel dysarthric speech; c) novel Spectral basis GAN-based
adversarial data augmentation operating on non-parallel data. Experiments
conducted on the UASpeech corpus suggest GAN-based data augmentation
consistently outperforms fine-tuned Wav2vec2.0 and HuBERT models using no data
augmentation and speed perturbation across different data expansion operating
points by statistically significant word error rate (WER) reductions up to
2.01% and 0.96% absolute (9.03% and 4.63% relative) respectively on the
UASpeech test set of 16 dysarthric speakers. After cross-system outputs
rescoring, the best system produced the lowest published WER of 16.53% (46.47%
on very low intelligibility) on UASpeech.
