Liu, T., Kukanov, I., Pan, Z., Wang, Q., Sailor, H. B., & Lee, K. A. (2024). Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing. 2024 IEEE Spoken Language Technology Workshop (SLT), 1185–1192. https://doi.org/10.1109/slt61566.2024.10832142
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Prime Minister’s Office, Singapore, and the Ministry of Digital Development and Information - Online Trust and Safety (OTS) Research Programme
Grant Reference no. : MCI-OTS-001