# Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models

arXiv: https://arxiv.org/abs/2309.12763 · HTML: https://mlsanity.com/paper/2309.12763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Generative Pre-Training for Speech with Autoregressive Predictive Coding](https://mlsanity.com/paper/1910.12607/) (2019)
- [Vector-Quantized Autoregressive Predictive Coding](https://mlsanity.com/paper/2005.08392/) (2020)
- [Data Augmenting Contrastive Learning of Speech Representations in the Time Domain](https://mlsanity.com/paper/2007.00991/) (2020)
- [Improved Speech Representations with Multi-Target Autoregressive Predictive Coding](https://mlsanity.com/paper/2004.05274/) (2020)
- [A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2201.08930/) (2022)
- [SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training](https://mlsanity.com/paper/2201.10207/) (2022)
- [Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition](https://mlsanity.com/paper/2110.04934/) (2021)
- [Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction](https://mlsanity.com/paper/2110.15430/) (2021)
- [SQuId: Measuring Speech Naturalness in Many Languages](https://mlsanity.com/paper/2210.06324/) (2022)
- [Wav2Vec-Aug: Improved self-supervised training with limited data](https://mlsanity.com/paper/2206.13654/) (2022)

## Abstract (arXiv)

Self-supervised representation learning (SSRL) has demonstrated superior performance than supervised models for tasks including phoneme recognition. Training SSRL models poses a challenge for low-resource languages where sufficient pre-training data may not be available. A common approach is cross-lingual pre-training. Instead, we propose to use audio augmentation techniques, namely: pitch variation, noise addition, accented target language and other language speech to pre-train SSRL models in a low resource condition and evaluate phoneme recognition. Our comparisons found that a combined synthetic augmentations (noise/pitch) strategy outperformed accent and language knowledge transfer. Furthermore, we examined the scaling factor of augmented data to achieve equivalent performance to model pre-trained with target domain speech. Our findings suggest that for resource-constrained languages, combined augmentations can be a viable option than other augmentations.