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

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arxiv 2409.14494 v4 pith:QI4LUU5U submitted 2024-09-13 cs.CL cs.LGcs.SDeess.AS

Ahmed Adel Attia , Dorottya Demszky , Tolulope Ogunremi , Jing Liu , Carol Espy-Wilson This is my paper · ORCID
classification cs.CLcs.LGcs.SDeess.AS
keywords classroomwav2vec2conditionsrecognitionrobustspeechadaptingautomatic
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research

    cs.SD 2025-06 conditional novelty 6.0 of 10

    SimClass is a new 391-hour simulated classroom speech dataset with game-engine babble noise; ASR fine-tuning on it beats Librispeech and TEDLIUM on real classroom test sets.

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