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

Parameter impaired speech utterances of single word commands or fine-tuning is often used to exploit the large quantities of nonaged short phrases. A similar study that performed architecture adaptation and healthy speech pre-trained models, while neural architecture was conducted on CTC-based CNN ASR systems [18] hyper-parameters are set using expert knowledge and remain for multilingual speech recognition, revealing that optimal convolutional unchanged. This paper investigates hyper-parameter adaptation module hyper-parameters, e.g. the convolution kernal for Conformer ASR systems that are pre-trained on the size, vary substantially between languages. In contrast, the Librispeech corpus before being domain adapted to the DementiaBank hyper-parameters domain adaptation of state-of-the-art end-toend elderly and UASpeech dysarthric speech datasets. Experimental ASR systems represented by, for example, those based on results suggest that hyper-parameter adaptation produced Conformer models [19-26], remains unvisited for dysarthric word error rate (WER) reductions of 0.45% and 0.67% and elderly speech recognition.