Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation

Wang, Disong; Liu, Songxiang; Wu, Xixin; Lu, Hui; Sun, Lifa; Liu, Xunying; Meng, Helen

doi:10.1109/icassp43922.2022.9746680

Cited by 4 publications

(1 citation statement)

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“…In contrast, only a few prior researches on applying GAN to dysarthric speech processing tasks, represented by dysarthric speech augmentation [109]- [112] and dysarthric speech reconstruction [113], [114] have been conducted to date. To the best of our knowledge, no previous study on GAN based data augmentation approaches designed for elderly speech recognition tasks has been conducted.…”

Section: Corpusmentioning

confidence: 99%

Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition

Jin¹,

Deng²,

Wang³

et al. 2022

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Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech, accurate recognition of dysarthric and elderly speech remains highly challenging tasks to date. It is difficult to collect large quantities of such data for ASR system development due to the mobility issues often found among these users. To this end, data augmentation techniques play a vital role. In contrast to existing data augmentation techniques only modifying the speaking rate or overall shape of spectral contour, fine-grained spectro-temporal differences between dysarthric, elderly and normal speech are modelled using a novel set of speaker dependent (SD) generative adversarial networks (GAN) based data augmentation approaches in this paper. These flexibly allow both: a) temporal or speed perturbed normal speech spectra to be modified and closer to those of an impaired speaker when parallel speech data is available; and b) for non-parallel data, the SVD decomposed normal speech spectral basis features to be transformed into those of a target elderly speaker before being re-composed with the temporal bases to produce the augmented data for state-of-the-art TDNN and Conformer ASR system training. Experiments are conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets. The proposed GAN based data augmentation approaches consistently outperform the baseline speed perturbation method by up to 0.91% and 3.0% absolute (9.61% and 6.4% relative) WER reduction on the TORGO and DementiaBank data respectively. Consistent performance improvements are retained after applying LHUC based speaker adaptation.

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