  Automatic recognition of disordered speech remains a highly challenging task
to date. Sources of variability commonly found in normal speech including
accent, age or gender, when further compounded with the underlying causes of
speech impairment and varying severity levels, create large diversity among
speakers. To this end, speaker adaptation techniques play a vital role in
current speech recognition systems. Motivated by the spectro-temporal level
differences between disordered and normal speech that systematically manifest
in articulatory imprecision, decreased volume and clarity, slower speaking
rates and increased dysfluencies, novel spectro-temporal subspace basis
embedding deep features derived by SVD decomposition of speech spectrum are
proposed to facilitate both accurate speech intelligibility assessment and
auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN and
end-to-end disordered speech recognition systems. Experiments conducted on the
UASpeech corpus suggest the proposed spectro-temporal deep feature adapted
systems consistently outperformed baseline i-Vector adaptation by up to 2.63%
absolute (8.6% relative) reduction in word error rate (WER) with or without
data augmentation. Learning hidden unit contribution (LHUC) based speaker
adaptation was further applied. The final speaker adapted system using the
proposed spectral basis embedding features gave an overall WER of 25.6% on the
UASpeech test set of 16 dysarthric speakers
