aadel4/Wav2vec_Classroom

Model Card: Wav2vec-Classroom

Model Overview

Model Name: Wav2vec-Classroom Version: 1.0 Developed By: Ahmed Adel Attia (University of Maryland & Stanford University) Date: 2025

Description: Wav2vec-Classroom is an automatic speech recognition (ASR) model designed for robust performance in classroom environments. The model is adapted from Wav2vec2.0 using Continued Pretraining (CPT) on large-scale unlabeled classroom audio data, followed by fine-tuning on a small set of transcribed classroom recordings. This approach enhances the model’s ability to handle classroom noise, overlapping speech, and diverse microphone setups.

Use Case:

• —Speech-to-text transcription for classroom recordings.
• —Automatic feedback generation for educational AI tools.
• —ASR research in low-resource, noisy environments.

Model Details

Architecture: Wav2vec2.0-based self-supervised model, fine-tuned with Fairseq

Training Data:

• —Unlabeled Classroom Audio (NCTE dataset): 5235 hours of classroom recordings used for self-supervised CPT.
• —NCTE-Gold: 5.15 hours of human-verified classroom transcriptions for supervised fine-tuning.

Training Strategy:

1. 1.Continued Pretraining (CPT): The model is initialized with a pre-trained Wav2vec2.0 checkpoint and further pre-trained on 5235 hours of unlabeled classroom speech data. This step allows the model to learn domain-specific acoustic representations.
2. 2.Supervised Fine-tuning: The CPT-pretrained model is then fine-tuned using the NCTE-Gold dataset for better alignment with transcriptions.

Evaluation Results

Word Error Rate (WER) comparison on NCTE and MPT test sets:

Limitations

• —The model is optimized for classroom speech and may not generalize well to other domains.
• —Background noise, overlapping speech, and speaker variations may still impact performance.
• —The amount of labeled training data remains limited, which may affect ASR accuracy in extreme cases.

Usage Request

If you use the Wav2vec-Classroom model in your research, please acknowledge this work and cite the following paper:

CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments Ahmed Adel Attia, Dorottya Demszky, Tolulopé Ògúnrẹ̀mí, Jing Liu, Carol Espy-Wilson ICASSP 2025

@article{attia2024cpt_wav2vec,
  title={CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments},
  author={Ahmed Adel Attia and Dorottya Demszky and Tolulopé Ògúnrẹ̀mí and Jing Liu and Carol Espy-Wilson},
  journal={ICASSP 2025},
  year={2024}
}