# Unsupervised Data Selection via Discrete Speech Representation for ASR

arXiv: https://arxiv.org/abs/2204.01981 · HTML: https://mlsanity.com/paper/2204.01981/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2022.

## Built on by (most cited first, 10 of 10)

- [OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning](https://mlsanity.com/paper/2506.00338/) (2025)
- [Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness](https://mlsanity.com/paper/2305.05095/) (2023)
- [On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models](https://mlsanity.com/paper/2406.09282/) (2024)
- [Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition](https://mlsanity.com/paper/2211.04717/) (2022)
- [Towards Selection of Text-to-speech Data to Augment ASR Training](https://mlsanity.com/paper/2306.00998/) (2023)
- [OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework](https://mlsanity.com/paper/2606.08574/) (2026)
- [Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models](https://mlsanity.com/paper/2212.01661/) (2022)
- [Speech Corpora Divergence Based Unsupervised Data Selection for ASR](https://mlsanity.com/paper/2302.13222/) (2023)
- [LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models Using in-the-wild Data](https://mlsanity.com/paper/2506.04586/) (2025)
- [Improving Large-Scale Weakly Supervised ASR by Filtering and Selection](https://mlsanity.com/paper/2606.28728/) (2026)

## Abstract (arXiv)

Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR). In this paper, we show that data selection is important for self-supervised learning. We propose a simple and effective unsupervised data selection method which selects acoustically similar speech to a target domain. It takes the discrete speech representation available in common self-supervised learning frameworks as input, and applies a contrastive data selection method on the discrete tokens. Through extensive empirical studies we show that our proposed method reduces the amount of required pre-training data and improves the downstream ASR performance. Pre-training on a selected subset of 6% of the general data pool results in 11.8% relative improvements in LibriSpeech test-other compared to pre-training on the full set. On Multilingual LibriSpeech French, German, and Spanish test sets, selecting 6% data for pre-training reduces word error rate by more than 15% relatively compared to the full set, and achieves competitive results compared to current state-of-the-art performances.