🤖 AI Summary
This work addresses the performance limitations of automatic speech recognition (ASR) for low-resource languages such as Swahili, which stem from severe scarcity of labeled training data. To mitigate this challenge, the authors propose a reproducible continual pre-training framework that leverages unlabeled audio through pseudo-labeling, followed by supervised fine-tuning, using only 20,000 labeled utterances. Built upon the wav2vec2-bert-2.0 architecture, the approach achieves a word error rate (WER) of 3.24% on the Common Voice Swahili test set—representing an 82% relative improvement over the baseline and a 61% reduction compared to the previous state-of-the-art academic system (8.3% WER). This substantial gain demonstrates a significant decrease in reliance on annotated data while maintaining high recognition accuracy.
📝 Abstract
We investigate continued pretraining (CPT) for adapting wav2vec2-bert-2.0 to Swahili automatic speech recognition (ASR). Our approach combines unlabeled audio with limited labeled data through pseudo-labeled CPT followed by supervised finetuning. With 20,000 labeled samples, we achieve 3.24% WER on Common Voice Swahili-an 82% relative improvement over the baseline. This result surpasses the best previously reported academic system (8.3% WER from XLS-R) by 61% relative improvement. We provide concrete data requirements and a replicable methodology applicable to other low-resource languages.