  Dysarthric speech recognition has posed major challenges due to lack of
training data and heavy mismatch in speaker characteristics. Recent ASR systems
have benefited from readily available pretrained models such as wav2vec2 to
improve the recognition performance. Speaker adaptation using fMLLR and
xvectors have provided major gains for dysarthric speech with very little
adaptation data. However, integration of wav2vec2 with fMLLR features or
xvectors during wav2vec2 finetuning is yet to be explored. In this work, we
propose a simple adaptation network for fine-tuning wav2vec2 using fMLLR
features. The adaptation network is also flexible to handle other speaker
adaptive features such as xvectors. Experimental analysis show steady
improvements using our proposed approach across all impairment severity levels
and attains 57.72\% WER for high severity in UASpeech dataset. We also
performed experiments on German dataset to substantiate the consistency of our
proposed approach across diverse domains.
