Speaker adaptation for Wav2vec2 based dysarthric ASR

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.