mlsanityData to 7 Oct 2026

Dynamic Data Pruning for Automatic Speech Recognition

arXiv 2406.18373 ↗First posted 26 Jun 2024cs.CL cs.SD eess.AScited by 4

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Abstract (arXiv)

The recent success of Automatic Speech Recognition (ASR) is largely attributed to the ever-growing amount of training data. However, this trend has made model training prohibitively costly and imposed computational demands. While data pruning has been proposed to mitigate this issue by identifying a small subset of relevant data, its application in ASR has been barely explored, and existing works often entail significant overhead to achieve meaningful results. To fill this gap, this paper presents the first investigation of dynamic data pruning for ASR, finding that we can reach the full-data performance by dynamically selecting 70% of data. Furthermore, we introduce Dynamic Data Pruning for ASR (DDP-ASR), which offers several fine-grained pruning granularities specifically tailored for speech-related datasets, going beyond the conventional pruning of entire time sequences. Our intensive experiments show that DDP-ASR can save up to 1.6x training time with negligible performance loss.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 26 Jun 2024.

Builds on

23 of 30 references.

References · 23

  1. 2020Scaling Laws for Neural Language Modelscited by 6,411
  2. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  5. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  6. 2023Textbooks Are All You Needcited by 561
  7. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 529
  8. 2024LESS: Selecting Influential Data for Targeted Instruction Tuningcited by 514
  9. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 337
  10. 2018Not All Samples Are Created Equal: Deep Learning with Importance Samplingcited by 323
  11. 2022Scaling Language-Image Pre-training via Maskingcited by 317
  12. 2018LRS3-TED: a large-scale dataset for visual speech recognitioncited by 301
11 more
  1. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 295
  2. 2022Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learntcited by 233
  3. 2021End-to-end Audio-visual Speech Recognition with Conformerscited by 147
  4. 2021Deep Learning Through the Lens of Example Difficultycited by 139
  5. 2023Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labelscited by 113
  6. 2020When Do Curricula Work?cited by 105
  7. 2022Visual Speech Recognition for Multiple Languages in the Wildcited by 94
  8. 2023InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruningcited by 90
  9. 2023Large-scale Dataset Pruning with Dynamic Uncertaintycited by 63
  10. 2021Accelerating Deep Learning with Dynamic Data Pruningcited by 43
  11. 2023Cluster-based pruning techniques for audio datacited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Are Sparse Neural Networks Better Hard Sample Learners?cited by 1
  2. 2025An Effective Training Framework for Light-Weight Automatic Speech Recognition Modelscited by 1
  3. 2025Extending Dataset Pruning to Object Detection: A Variance-based Approachcited by 0
  4. 2026OPERA: Online Data Pruning for Efficient Retrieval Model Adaptationcited by 0