  This study addresses the speech enhancement (SE) task within the causal
inference paradigm by modeling the noise presence as an intervention. Based on
the potential outcome framework, the proposed causal inference-based speech
enhancement (CISE) separates clean and noisy frames in an intervened noisy
speech using a noise detector and assigns both sets of frames to two mask-based
enhancement modules (EMs) to perform noise-conditional SE. Specifically, we use
the presence of noise as guidance for EM selection during training, and the
noise detector selects the enhancement module according to the prediction of
the presence of noise for each frame. Moreover, we derived a SE-specific
average treatment effect to quantify the causal effect adequately. Experimental
evidence demonstrates that CISE outperforms a non-causal mask-based SE approach
in the studied settings and has better performance and efficiency than more
complex SE models.
