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Automatic recognition of disordered and elderly speech remains highlychallenging tasks to date due to data scarcity. Parameter fine-tuning is oftenused to exploit the large quantities of non-aged and healthy speech pre-trainedmodels, while neural architecture hyper-parameters are set using expertknowledge and remain unchanged. This paper investigates hyper-parameteradaptation for Conformer ASR systems that are pre-trained on the Librispeechcorpus before being domain adapted to the DementiaBank elderly and UASpeechdysarthric speech datasets. Experimental results suggest that hyper-parameteradaptation produced word error rate (WER) reductions of 0.45% and 0.67% overparameter-only fine-tuning on DBank and UASpeech tasks respectively. Anintuitive correlation is found between the performance improvements byhyper-parameter domain adaptation and the relative utterance length ratiobetween the source and target domain data.

Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition