  Dysarthric speech recognition is a challenging task as dysarthric data is
limited and its acoustics deviate significantly from normal speech. Model-based
speaker adaptation is a promising method by using the limited dysarthric speech
to fine-tune a base model that has been pre-trained from large amounts of
normal speech to obtain speaker-dependent models. However, statistic
distribution mismatches between the normal and dysarthric speech data limit the
adaptation performance of the base model. To address this problem, we propose
to re-initialize the base model via meta-learning to obtain a better model
initialization. Specifically, we focus on end-to-end models and extend the
model-agnostic meta learning (MAML) and Reptile algorithms to meta update the
base model by repeatedly simulating adaptation to different dysarthric
speakers. As a result, the re-initialized model acquires dysarthric speech
knowledge and learns how to perform fast adaptation to unseen dysarthric
speakers with improved performance. Experimental results on UASpeech dataset
show that the best model with proposed methods achieves 54.2% and 7.6% relative
word error rate reduction compared with the base model without finetuning and
the model directly fine-tuned from the base model, respectively, and it is
comparable with the state-of-the-art hybrid DNN-HMM model.
