A Comparison of Speech Data Augmentation Methods Using S3PRL Toolkit
This work provides an incremental comparison of augmentation techniques for speech recognition tasks, benefiting researchers in speech processing.
The study compared data augmentation methods for speech processing using the S3PRL toolkit, finding that SpecAugment slightly improved HuBERT and wav2vec models on original datasets, while Gaussian Noise and Speed Perturbation enhanced robustness on augmented test sets.
Data augmentations are known to improve robustness in speech-processing tasks. In this study, we summarize and compare different data augmentation strategies using S3PRL toolkit. We explore how HuBERT and wav2vec perform using different augmentation techniques (SpecAugment, Gaussian Noise, Speed Perturbation) for Phoneme Recognition (PR) and Automatic Speech Recognition (ASR) tasks. We evaluate model performance in terms of phoneme error rate (PER) and word error rate (WER). From the experiments, we observed that SpecAugment slightly improves the performance of HuBERT and wav2vec on the original dataset. Also, we show that models trained using the Gaussian Noise and Speed Perturbation dataset are more robust when tested with augmented test sets.