Data Augmentation Using Spectral Warping for Low Resource Children ASR

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Journal of Signal Processing Systems
In low resource children automatic speech recognition (ASR) the performance is degraded due to limited acoustic and speaker variability available in small datasets. In this paper, we propose a spectral warping based data augmentation method to capture more acoustic and speaker variability. This is carried out by warping the linear prediction (LP) spectra computed from speech data. The warped LP spectra computed in a frame-based manner are used with the corresponding LP residuals to synthesize speech to capture more variability. The proposed augmentation method is shown to improve the ASR system performance over the baseline system. We have compared the proposed method with four well-known data augmentation methods: pitch scaling, speaking rate, SpecAug and vocal tract length perturbation (VTLP), and found that the proposed method performs the best. Further, we have combined the proposed method with these existing data augmentation methods to improve the ASR system performance even more. The combined system consisting of the original data, VTLP, SpecAug and the proposed spectral warping method gave the best performance by a relative word error rate reduction of 32.13% and 10.51% over the baseline system for Punjabi children and TLT-school corpus, respectively. The proposed spectral warping method is publicly available at
Funding Information: This work was supported by the Academy of Finland (grants 329267, 330139). Publisher Copyright: © 2022, The Author(s).
Children speech recognition, Prosody modification, SpecAug, Spectral warping, TDNN, VTLP
Other note
Kathania, H K, Kadyan, V, Kadiri, S R & Kurimo, M 2022, ' Data Augmentation Using Spectral Warping for Low Resource Children ASR ', Journal of Signal Processing Systems, vol. 94, no. 12, pp. 1507-1513 .