Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing
<|reference_start|>Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing: 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.<|reference_end|>
@article{liu2024towards,
    title={Towards Quantifying and Reducing Language Mismatch Effects in
  Cross-Lingual Speech Anti-Spoofing},
    author={Tianchi Liu, Ivan Kukanov, Zihan Pan, Qiongqiong Wang, Hardik B.
  Sailor, Kong Aik Lee},
    journal={arXiv preprint arXiv:2409.08346},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08346},
    primaryClass={eess.AS cs.AI cs.CL cs.SD}
}