Abstract
Automatic speech recognition (ASR) plays a vital role in enabling natural human-machine interaction across applications such as virtual assistants, industrial automation, customer support, and real-time transcription. However, developing accurate ASR systems for low-resource languages like Arabic remains a significant challenge due to limited labeled data and the linguistic complexity introduced by diverse dialects. In this work, we present a scalable training pipeline that combines weakly supervised learning with supervised fine-tuning to develop a robust Arabic ASR model. In the first stage, we pretrain the model on 15,000 hours of weakly labeled speech covering both Modern Standard Arabic (MSA) and various Dialectal Arabic (DA) variants. In the subsequent stage, we perform continual supervised fine-tuning using a mixture of filtered weakly labeled data and a small, high-quality annotated dataset. Our approach achieves state-of-the-art results, ranking first in the multi-dialectal Arabic ASR challenge. These findings highlight the effectiveness of weak supervision paired with fine-tuning in overcoming data scarcity and delivering high-quality ASR for low-resource, dialect-rich languages.
Abstract (translated)
自动语音识别（ASR）在虚拟助手、工业自动化、客户支持和实时转录等应用中发挥着关键作用，以实现自然的人机交互。然而，对于像阿拉伯语这样资源较少的语言来说，开发准确的ASR系统仍然面临巨大挑战，原因在于标注数据有限以及由多种方言引起的语言复杂性增加。在这项工作中，我们提出了一种可扩展的训练管道，结合了弱监督学习和有监督微调来开发一个稳健的阿拉伯语ASR模型。 在第一阶段，我们在涵盖现代标准阿拉伯语（MSA）和各种方言阿拉伯语（DA）变体的15,000小时弱标注语音数据上预训练模型。随后，在第二阶段中，我们使用过滤后的弱标注数据与小规模高质量注释数据集进行持续有监督微调。 我们的方法取得了最先进的结果，并在多方言阿拉伯语ASR挑战赛中排名第一。这些发现突显了弱监督结合微调的有效性，能够克服数据稀缺问题并为低资源、方言丰富的语言提供高质量的ASR系统。
URL
https://arxiv.org/abs/2508.08912
