PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition
ACM Multimedia Asia(2023)
Abstract
Most of the current speech data augmentation methods operate on either the
raw waveform or the amplitude spectrum of speech. In this paper, we propose a
novel speech data augmentation method called PhasePerturbation that operates
dynamically on the phase spectrum of speech. Instead of statically rotating a
phase by a constant degree, PhasePerturbation utilizes three dynamic phase
spectrum operations, i.e., a randomization operation, a frequency masking
operation, and a temporal masking operation, to enhance the diversity of speech
data. We conduct experiments on wav2vec2.0 pre-trained ASR models by
fine-tuning them with the PhasePerturbation augmented TIMIT corpus. The
experimental results demonstrate 10.9\% relative reduction in the word error
rate (WER) compared with the baseline model fine-tuned without any augmentation
operation. Furthermore, the proposed method achieves additional improvements
(12.9\% and 15.9\%) in WER by complementing the Vocal Tract Length Perturbation
(VTLP) and the SpecAug, which are both amplitude spectrum-based augmentation
methods. The results highlight the capability of PhasePerturbation to improve
the current amplitude spectrum-based augmentation methods.
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