  Automatic recognition of disordered speech remains a highly challenging task
to date. The underlying neuro-motor conditions, often compounded with
co-occurring physical disabilities, lead to the difficulty in collecting large
quantities of impaired speech required for ASR system development. This paper
presents novel variational auto-encoder generative adversarial network
(VAE-GAN) based personalized disordered speech augmentation approaches that
simultaneously learn to encode, generate and discriminate synthesized impaired
speech. Separate latent features are derived to learn dysarthric speech
characteristics and phoneme context representations. Self-supervised
pre-trained Wav2vec 2.0 embedding features are also incorporated. Experiments
conducted on the UASpeech corpus suggest the proposed adversarial data
augmentation approach consistently outperformed the baseline speed perturbation
and non-VAE GAN augmentation methods with trained hybrid TDNN and End-to-end
Conformer systems. After LHUC speaker adaptation, the best system using VAE-GAN
based augmentation produced an overall WER of 27.78% on the UASpeech test set
of 16 dysarthric speakers, and the lowest published WER of 57.31% on the subset
of speakers with "Very Low" intelligibility.
