From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data

• URL: http://arxiv.org/abs/2505.17088v1
• Date: Tue, 20 May 2025 20:50:28 GMT
• Title: From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data
• Authors: Ahmed Adel Attia, Dorottya Demszky, Jing Liu, Carol Espy-Wilson,
• Abstract summary: Weakly Supervised Pretraining is a two-step process where models are first pretrained on weak transcripts in a supervised manner, and then fine-tuned on accurate data.<n>Our results, based on both synthetic and real weak transcripts, show that WSP outperforms alternative methods.
• Score: 4.577418871605078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in speech recognition has relied on models trained on vast amounts of labeled data. However, classroom Automatic Speech Recognition (ASR) faces the real-world challenge of abundant weak transcripts paired with only a small amount of accurate, gold-standard data. In such low-resource settings, high transcription costs make re-transcription impractical. To address this, we ask: what is the best approach when abundant inexpensive weak transcripts coexist with limited gold-standard data, as is the case for classroom speech data? We propose Weakly Supervised Pretraining (WSP), a two-step process where models are first pretrained on weak transcripts in a supervised manner, and then fine-tuned on accurate data. Our results, based on both synthetic and real weak transcripts, show that WSP outperforms alternative methods, establishing it as an effective training methodology for low-resource ASR in real-world scenarios.

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