mlsanityData to 14 Oct 2026

Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings

arXiv 2405.13018 ↗First posted 15 May 2024cs.CL cs.AI eess.AScited by 4

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Abstract (arXiv)

Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of continued pretraining (CPT) in adapting Wav2vec2.0 to the classroom domain. We show that CPT is a powerful tool in that regard and reduces the Word Error Rate (WER) of Wav2vec2.0-based models by upwards of 10%. More specifically, CPT improves the model's robustness to different noises, microphones, classroom conditions as well as classroom demographics. Our CPT models show improved ability to generalize to different demographics unseen in the labeled finetuning data.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 15 May 2024.

Builds on

13 of 41 references.

References · 13

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  4. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
  5. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 501
  6. 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 151
  7. 2022The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcriptscited by 43
  8. 2020End-to-End Multi-speaker Speech Recognition with Transformercited by 42
  9. 2018End-to-End Monaural Multi-speaker ASR System without Pretrainingcited by 26
  10. 2023Adaptation of Whisper models to child speech recognitioncited by 26
  11. 2024Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokenscited by 11
  12. 2024Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representationcited by 7
1 more
  1. 2023Self-Supervised Adaptive AV Fusion Module for Pre-Trained ASR Modelscited by 6

Built on by

Cited by year (2026 to 14 Oct)

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

4 papers.

  1. 2025Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languagescited by 3
  2. 2026Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Datacited by 2
  3. 2024Examining Test-Time Adaptation for Personalized Child Speech Recognitioncited by 0
  4. 2025Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Contextcited by 0