  We investigate the performance of self-supervised pretraining frameworks on
pathological speech datasets used for automatic speech recognition (ASR).
Modern end-to-end models require thousands of hours of data to train well, but
only a small number of pathological speech datasets are publicly available. A
proven solution to this problem is by first pretraining the model on a huge
number of healthy speech datasets and then fine-tuning it on the pathological
speech datasets. One new pretraining framework called self-supervised learning
(SSL) trains a network using only speech data, providing more flexibility in
training data requirements and allowing more speech data to be used in
pretraining. We investigate SSL frameworks such as the wav2vec 2.0 and WavLM
models using different setups and compare their performance with different
supervised pretraining setups, using two types of pathological speech, namely,
Japanese electrolaryngeal and English dysarthric. Our results show that
although SSL has shown success with minimally resourced healthy speech, we do
not find this to be the case with pathological speech. The best supervised
setup outperforms the best SSL setup by 13.9% character error rate in
electrolaryngeal speech and 16.8% word error rate in dysarthric speech.
