  Most research in synthetic speech detection (SSD) focuses on improving
performance on standard noise-free datasets. However, in actual situations,
noise interference is usually present, causing significant performance
degradation in SSD systems. To improve noise robustness, this paper proposes a
dual-branch knowledge distillation synthetic speech detection (DKDSSD) method.
Specifically, a parallel data flow of the clean teacher branch and the noisy
student branch is designed, and interactive fusion module and response-based
teacher-student paradigms are proposed to guide the training of noisy data from
both the data distribution and decision-making perspectives. In the noisy
student branch, speech enhancement is introduced initially for denoising,
aiming to reduce the interference of strong noise. The proposed interactive
fusion combines denoised features and noisy features to mitigate the impact of
speech distortion and ensure consistency with the data distribution of the
clean branch. The teacher-student paradigm maps the student's decision space to
the teacher's decision space, enabling noisy speech to behave similarly to
clean speech. Additionally, a joint training method is employed to optimize
both branches for achieving global optimality. Experimental results based on
multiple datasets demonstrate that the proposed method performs effectively in
noisy environments and maintains its performance in cross-dataset experiments.
Source code is available at https://github.com/fchest/DKDSSD.
