Automatic recognition of disordered and elderly speech remains highly
challenging tasks to date due to data scarcity. Parameter fine-tuning is often
used to exploit the large quantities of non-aged and healthy speech pre-trained
models, while neural architecture hyper-parameters are set using expert
knowledge and remain unchanged. This paper investigates hyper-parameter
adaptation for Conformer ASR systems that are pre-trained on the Librispeech
corpus before being domain adapted to the DementiaBank elderly and UASpeech
dysarthric speech datasets. Experimental results suggest that hyper-parameter
adaptation produced word error rate (WER) reductions of 0.45% and 0.67% over
parameter-only fine-tuning on DBank and UASpeech tasks respectively. An
intuitive correlation is found between the performance improvements by
hyper-parameter domain adaptation and the relative utterance length ratio
between the source and target domain data.Comment: 5 pages, 3 figures, 3 tables, accepted by Interspeech202