Lightweight speech enhancement via learnable prior and Schrödinger bridge generative adversarial network

This paper introduces a different speech enhancement framework that integrates learnable prior with a Schrödinger bridge generative adversarial network. Although conventional Schrödinger bridge-based speech enhancement methods have shown promising results, they suffer from inefficient transport paths as a result of path crossings, high computational requirements, and degraded speech quality in resource-constrained scenarios. The proposed approach overcomes these limitations by synergistically combining learnable prior and adversarial modeling paradigms. The learnable prior module effectively captures fundamental speech characteristics and dynamically adjusts the initial distribution during training, thereby minimizing path crossings and facilitating more efficient transport paths. The architecture incorporates adversarial training and multi-scale loss functions to enhance speech quality and naturalness. Extensive experimental evaluations demonstrate that this method outperforms state-of-the-art approaches across various metrics, including overall quality (OVRL), speech quality (SIG), background noise quality (BAK), and P808.MOS, while maintaining computational efficiency. Remarkably, this approach demonstrates robust performance even in resource-constrained environments with the nano-sized model (0.04 M parameters) delivering competitive results. Comprehensive ablation studies confirm the efficacy of each component and offer valuable insights into the role of learnable priors in speech enhancement applications.