# CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments

arXiv: https://arxiv.org/abs/2409.14494 · HTML: https://mlsanity.com/paper/2409.14494/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [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)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts](https://mlsanity.com/paper/2211.11772/) (2022)
- [Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults](https://mlsanity.com/paper/2309.07927/) (2023)
- [Adaptation of Whisper models to child speech recognition](https://mlsanity.com/paper/2307.13008/) (2023)
- [A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2201.08930/) (2022)
- [Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining](https://mlsanity.com/paper/2301.07295/) (2023)
- [Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens](https://mlsanity.com/paper/2402.02302/) (2024)
- [Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek](https://mlsanity.com/paper/2301.00304/) (2022)
- [Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation](https://mlsanity.com/paper/2401.03468/) (2024)
- [Self-Supervised Adaptive AV Fusion Module for Pre-Trained ASR Models](https://mlsanity.com/paper/2312.13873/) (2023)

## Built on by (most cited first, 7 of 7)

- [Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASR](https://mlsanity.com/paper/2601.20142/) (2026)
- [CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning](https://mlsanity.com/paper/2506.17818/) (2025)
- [SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research](https://mlsanity.com/paper/2506.09206/) (2025)
- [Multi-Stage Speaker Diarization for Noisy Classrooms](https://mlsanity.com/paper/2505.10879/) (2025)
- [From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data](https://mlsanity.com/paper/2505.17088/) (2025)
- [RealClass: A Framework for Classroom Speech Simulation with Public Datasets and Game Engines](https://mlsanity.com/paper/2510.01462/) (2025)
- [Speech Separation for Hearing-Impaired Children in the Classroom](https://mlsanity.com/paper/2511.07677/) (2025)

## 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 and classroom conditions.