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.
Abstract (translated)
我们研究了用于将wav2vec2-bert-2.0模型适应斯瓦希里语自动语音识别（ASR）的持续预训练（CPT）。我们的方法结合了未标记音频和有限标签数据，通过伪标注CPT然后进行监督微调。使用20,000个标注样本，在Common Voice 斯瓦希里语测试集上实现了3.24%的WER，相较于基线模型相对改进了82%。这一结果超越了之前最佳的学术系统（XLS-R系统的8.3% WER），相对提高了61%。我们提供了具体的数据需求和可重复的方法论，适用于其他低资源语言。
URL
https://arxiv.org/abs/2603.11378
