低资源语言高效语音识别：利用跨语言无标签数据
Efficient ASR for Low-Resource Languages: Leveraging Cross-Lingual Unlabeled Data
AI总结：
通过跨语言无标签数据预训练，提升低资源语言语音识别性能，模型参数更少但效果更优。
摘要：
低资源语言的自动语音识别仍然受到标注数据稀缺和先进模型所需计算资源的限制。我们系统地研究了低资源语言的跨语言连续预训练，以波斯-阿拉伯语言（波斯语、阿拉伯语和乌尔都语）为主要案例研究。我们的方法表明，战略性地利用无标签语音数据可以有效弥补资源缺口而不牺牲识别准确性。我们通过可扩展的无标签数据收集管道构建了一个3000小时的多语言语料库，并采用针对性的持续预训练结合形态意识的分词方法，开发出一个3亿参数的模型，其性能可与5倍大的系统相媲美。我们的模型在波斯语上优于Whisper Large v3（1.5B参数），在阿拉伯语和乌尔都语上尽管使用显著更少的参数和大量更少的标注数据，也取得了竞争性的结果。这些发现挑战了现有假设，即语音识别质量主要随着模型大小而变化，揭示出数据相关性和战略性预训练对于低资源场景更为关键。这项工作提供了一条通往包容性语音技术的实用路径，使在代表性不足的语言上能够有效实现语音识别，而无需依赖大规模计算基础设施或专有数据集。
英文摘要：
Automatic speech recognition for low-resource languages remains fundamentally constrained by the scarcity of labeled data and computational resources required by state-of-the-art models. We present a systematic investigation into cross-lingual continuous pretraining for low-resource languages, using Perso-Arabic languages (Persian, Arabic, and Urdu) as our primary case study. Our approach demonstrates that strategic utilization of unlabeled speech data can effectively bridge the resource gap without sacrificing recognition accuracy. We construct a 3,000-hour multilingual corpus through a scalable unlabeled data collection pipeline and employ targeted continual pretraining combined with morphologically-aware tokenization to develop a 300M parameter model that achieves performance comparable to systems 5 times larger. Our model outperforms Whisper Large v3 (1.5B parameters) on Persian and achieves competitive results on Arabic and Urdu despite using significantly fewer parameters and substantially less labeled data. These findings challenge the prevailing assumption that ASR quality scales primarily with model size, revealing instead that data relevance and strategic pretraining are more critical factors for low-resource scenarios. This work provides a practical pathway toward inclusive speech technology, enabling effective ASR for underrepresented languages without dependence on massive computational infrastructure or proprietary datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
