迈向量化与降低跨语言语音反欺骗中的语言不匹配效应
Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing
AI总结：
本文针对跨语言语音反欺骗中的语言不匹配问题，提出基于口音的数据扩展方法ACCENT，在12种语言、超300万样本上验证其可将不匹配效应降低超过15%。
摘要：
语言不匹配效应会影响语音反欺骗系统，但对这些效应的研究与量化仍然有限。现有的反欺骗数据集主要以英语为主，而获取多语言数据集的高昂成本阻碍了训练语言无关模型。我们首先评估了在英语数据上训练但在其他语言上测试的顶尖语音反欺骗系统，观察到明显的性能下降。我们提出了一种创新方法——基于口音的数据扩展（ACCENT），该方法通过TTS为单语训练的模型引入多样化的语言知识，从而提升其跨语言能力。我们在一个包含超过300万个样本的大规模数据集上进行了实验，其中包括180万个训练样本和近120万个测试样本，覆盖12种语言。通过应用所提出的ACCENT方法，语言不匹配效应得到了初步量化，并显著降低了超过15%。这种易于实现的方法在多语言和低资源语言场景中展现出良好的前景。
英文摘要：
The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited. Existing anti-spoofing datasets are mainly in English, and the high cost of acquiring multilingual datasets hinders training language-independent models. We initiate this work by evaluating top-performing speech anti-spoofing systems that are trained on English data but tested on other languages, observing notable performance declines. We propose an innovative approach - Accent-based data expansion via TTS (ACCENT), which introduces diverse linguistic knowledge to monolingual-trained models, improving their cross-lingual capabilities. We conduct experiments on a large-scale dataset consisting of over 3 million samples, including 1.8 million training samples and nearly 1.2 million testing samples across 12 languages. The language mismatch effects are preliminarily quantified and remarkably reduced over 15% by applying the proposed ACCENT. This easily implementable method shows promise for multilingual and low-resource language scenarios.
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。
