Abstract
In this work, we investigate the Meta PL unsupervised domain adaptation framework for Automatic Speech Recognition (ASR). We introduce a Multi-Stage Domain Adaptation pipeline (MSDA), a sample-efficient, two-stage adaptation approach that integrates self-supervised learning with semi-supervised techniques. MSDA is designed to enhance the robustness and generalization of ASR models, making them more adaptable to diverse conditions. It is particularly effective for low-resource languages like Greek and in weakly supervised scenarios where labeled data is scarce or noisy. Through extensive experiments, we demonstrate that Meta PL can be applied effectively to ASR tasks, achieving state-of-the-art results, significantly outperforming state-of-the-art methods, and providing more robust solutions for unsupervised domain adaptation in ASR. Our ablations highlight the necessity of utilizing a cascading approach when combining self-supervision with self-training.
Abstract (translated)
在这项工作中，我们研究了用于自动语音识别（ASR）的元PL无监督领域适应框架。我们引入了一种多阶段领域适应流水线（MSDA），这是一种样本效率高的两阶段适应方法，它将自监督学习与半监督技术相结合。MSDA旨在增强ASR模型的鲁棒性和泛化能力，使其更能适应各种条件。该方法特别适用于希腊语等资源匮乏的语言以及标签数据稀缺或嘈杂的弱监督场景。 通过广泛的实验，我们展示了Meta PL可以有效地应用于ASR任务，并取得了最先进的成果，在很大程度上超越了现有的最佳方法，为无监督领域适应在ASR中的应用提供了更稳健的解决方案。我们的消融研究突显了在结合自监督和自我训练时采用级联方法的重要性。
URL
https://arxiv.org/abs/2505.24656
