Abstract
Automatic speech recognition (ASR) is crucial for human-machine interaction in diverse applications like conversational agents, industrial robotics, call center automation, and automated subtitling. However, developing high-performance ASR models remains challenging, particularly for low-resource languages like Arabic, due to the scarcity of large, labeled speech datasets, which are costly and labor-intensive to produce. In this work, we employ weakly supervised learning to train an Arabic ASR model using the Conformer architecture. Our model is trained from scratch on 15,000 hours of weakly annotated speech data covering both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), eliminating the need for costly manual transcriptions. Despite the absence of human-verified labels, our approach attains state-of-the-art (SOTA) performance, exceeding all previous efforts in the field of Arabic ASR on the standard benchmarks. By demonstrating the effectiveness of weak supervision as a scalable, cost-efficient alternative to traditional supervised approaches, paving the way for improved ASR systems in low resource settings.
Abstract (translated)
自动语音识别（ASR）在诸如对话代理、工业机器人、呼叫中心自动化和自动字幕生成等众多应用中，对于人机交互至关重要。然而，开发高性能的ASR模型仍然具有挑战性，尤其是在像阿拉伯语这样的低资源语言中，因为大而标注良好的语音数据集稀缺且制作成本高昂且耗时。在这项工作中，我们采用弱监督学习方法，使用Conformer架构训练一个阿拉伯语ASR模型。我们的模型从头开始在15,000小时的弱注释语音数据上进行训练，涵盖了现代标准阿拉伯语（MSA）和方言阿拉伯语（DA），从而消除了昂贵的人工转录需求。 尽管缺乏人工验证标签，但我们的方法达到了业内最先进的性能水平，在阿拉伯语ASR的标准基准测试中超过了所有先前的努力。通过证明弱监督作为一种可扩展且成本效益高的替代方案的有效性，传统监督方法在资源匮乏的环境下为改进ASR系统铺平了道路。
URL
https://arxiv.org/abs/2504.12254
