Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition
Refereed conference paper presented and published in conference proceedings
Officially Accepted for Publication
CUHK Authors
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AbstractAutomatic recognition of disordered and elderly speech remain 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 statistically significant 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.
Acceptance Date18/05/2023
All Author(s) ListTianzi Wang, Shoukang Hu, Jiajun Deng, Zengrui Jin, Mengzhe Geng, Yi Wang, Helen Meng, Xunying Liu
Name of ConferenceISCA Interspeech2023
Start Date of Conference20/08/2023
End Date of Conference24/08/2023
Place of ConferenceDublin
Country/Region of ConferenceIreland
Year2023
LanguagesEnglish-United States
