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arXiv · 2306.15265

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

Tianzi Wang·Shoukang Hu·Jiajun Deng·Zengrui Jin·Mengzhe Geng·Yi Wang·Helen Meng·Xunying Liu
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Abstract

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.

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Tianzi Wang, Shoukang Hu, Jiajun Deng, Zengrui Jin, Mengzhe Geng, Yi Wang, Helen Meng, Xunying Liu. 2023-06-27. Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition. https://arxiv.org/abs/2306.15265

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