A Hybrid Probabilistic-Deterministic Model Recursively Enhancing Speech

This paper introduces Probabilistic-Deterministic Recursive Enhancement (PDRE), an innovative iterative Speech Enhancement (SE) approach that integrates probabilistic and deterministic methodologies. Recent advancements in diffusion models have demonstrated the exceptional effectiveness of probabilistic model-based iterative estimation in SE, especially when combined with deterministic Neural Network (NN)-based methods. However, these models often require extensive iterations, leading to significant computational costs. To tackle this issue, we propose PDRE as a more efficient alternative. PDRE progressively refines the clean speech density estimates by recursively applying an Enhancement Network (EN), which is trained using a maximum likelihood objective. A single application of the EN can substantially improve the estimation, enabling PDRE to achieve high SE accuracy with significantly fewer iterations. Additionally, PDRE synergizes recursive enhancement with deterministic signal estimation, resulting in even greater accuracy. Our experiments demonstrate that PDRE significantly reduces iteration counts and computational costs compared to diffusion model-based SEs while maintaining or improving the remarkably high estimation accuracy.