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.

研究了预训练于 Librispeech 语料库上并经过领域适配后用于识别老年人和患有运动障碍的语音数据集的 Conformer ASR 系统的超参数自适应，证明其能够显著提高词错误率的表现。