XWSB: A Blend System Utilizing XLS-R and WavLM with SLS Classifier detection system for SVDD 2024 Challenge

TL;DR

The paper presents XWSB, a novel deepfake detection system combining XLS-R, WavLM, and SLS techniques, achieving state-of-the-art results in the inaugural SVDD 2024 Challenge for singing voice deepfake detection.

Contribution

Introduces the XWSB system that integrates XLS-R, WavLM, and SLS for improved singing voice deepfake detection, setting new performance benchmarks in the SVDD 2024 Challenge.

Findings

Achieved an EER of 2.32% in the SVDD challenge.

Demonstrated superior recognition capabilities over existing methods.

Validated effectiveness of combining XLS-R, WavLM, and SLS techniques.

Abstract

This paper introduces the model structure used in the SVDD 2024 Challenge. The SVDD 2024 challenge has been introduced this year for the first time. Singing voice deepfake detection (SVDD) which faces complexities due to informal speech intonations and varying speech rates. In this paper, we propose the XWSB system, which achieved SOTA per-formance in the SVDD challenge. XWSB stands for XLS-R, WavLM, and SLS Blend, representing the integration of these technologies for the purpose of SVDD. Specifically, we used the best performing model structure XLS-R&SLS from the ASVspoof DF dataset, and applied SLS to WavLM to form the WavLM&SLS structure. Finally, we integrated two models to form the XWSB system. Experimental results show that our system demonstrates advanced recognition capabilities in the SVDD challenge, specifically achieving an EER of 2.32% in the CtrSVDD track. The code and data can be found at https://github.com/QiShanZhang/XWSB_for_ SVDD2024.