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.
@article{arxiv.2306.15265,
title = {Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition},
author = {Tianzi Wang and Shoukang Hu and Jiajun Deng and Zengrui Jin and Mengzhe Geng and Yi Wang and Helen Meng and Xunying Liu},
journal= {arXiv preprint arXiv:2306.15265},
year = {2023}
}
Comments
5 pages, 3 figures, 3 tables, accepted by Interspeech2023