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Deep learning-based speech enhancement typically relies on paired data, creating a domain gap between training and deployment. Unsupervised methods based on generative adversarial networks (GANs) use unpaired data as source priors, but existing single-branch models often suffer from source leakage due to a dominant consistency loss or a weak clean speech prior. In this work, we present a comprehensive analysis of prior data in unsupervised GAN-based speech enhancement and utilize a dual-branch framework that explicitly models both clean speech and noise. Our experiments show that mismatched priors increase source leakage. By utilizing aligned noise prior data—which are easy to collect in practice—we significantly mitigate speech-noise leakage, prevent the over-suppression of speech, and improve perceptual quality. This demonstrates that leveraging environment-specific noise data is an effective strategy for improving unsupervised enhancement in target domains.