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arXiv · 2203.14593

On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition

Mengzhe Geng·Xurong Xie·Rongfeng Su·Jianwei Yu·Zengrui Jin·Tianzi Wang·Shujie Hu·Zi Ye·Helen Meng·Xunying Liu
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Abstract

Accurate recognition of dysarthric and elderly speech remain challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender, when aggregated with age and speech impairment, create large diversity among these speakers. Scarcity of speaker-level data limits the practical use of data-intensive model based speaker adaptation methods. To this end, this paper proposes two novel forms of data-efficient, feature-based on-the-fly speaker adaptation methods: variance-regularized spectral basis embedding (SVR) and spectral feature driven f-LHUC transforms. Experiments conducted on UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest the proposed on-the-fly speaker adaptation approaches consistently outperform baseline iVector adapted hybrid DNN/TDNN and E2E Conformer systems by statistically significant WER reduction of 2.48%-2.85% absolute (7.92%-8.06% relative), and offline model based LHUC adaptation by 1.82% absolute (5.63% relative) respectively.

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Mengzhe Geng, Xurong Xie, Rongfeng Su, Jianwei Yu, Zengrui Jin, Tianzi Wang, Shujie Hu, Zi Ye, Helen Meng, Xunying Liu. 2023-05-28. On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition. https://arxiv.org/abs/2203.14593

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