Speech Enhancement Based on Drifting Models
Abstract
We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative sampling, DriftSE natively achieves one-step inference by evolving the pushforward distribution of a mapping function to directly match the clean speech distribution. This evolution is driven by a Drifting Field, a learned correction vector that guides samples toward the high-density regions of the clean distribution, which naturally facilitates training on unpaired data by matching distributions rather than paired samples. We investigate the framework under two formulations: a direct mapping from the noisy observation, and a stochastic conditional generative model from a Gaussian prior. Experiments on the VoiceBank-DEMAND benchmark demonstrate that DriftSE achieves high-fidelity enhancement in a single step, outperforming multi-step diffusion baselines and establishing a new paradigm for speech enhancement.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of DriftSE, a novel generative framework for speech enhancement that reformulates denoising as an equilibrium problem, achieving high-fidelity results in a single inference step. This work represents a significant advancement in the field of speech enhancement, combining innovative methodology with robust experimental validation to address critical challenges in real-time applications.
Methodology
The proposed method, DriftSE, innovatively formulates speech enhancement as an equilibrium problem, leveraging a learned Drifting Field for one-step inference. This approach diverges from traditional iterative sampling techniques, providing a significant computational advantage. The use of a semantic latent space for drift computation enhances the model's ability to capture complex speech structures, which is a notable improvement over existing methods. The dual formulation of the model—direct mapping and conditional generation—adds flexibility and robustness to the framework, allowing it to adapt to various scenarios, including unpaired training.
Results & evaluation
The experiments conducted on the VoiceBank-DEMAND benchmark and the DNS Challenge 2020 blind test set showcase the effectiveness of DriftSE in achieving high-fidelity speech enhancement. The reported metrics (PESQ, SI-SDR, SCOREQ) indicate that DriftSE outperforms both multi-step diffusion models and other one-step approaches, establishing its competitive edge. The thorough evaluation across different datasets and conditions demonstrates the model's generalization capabilities, which is crucial for real-world applications.
Reproducibility
The paper provides detailed implementation specifics, including architecture choices, training procedures, and hyperparameter settings, which are essential for reproducibility. However, the absence of a public code repository or demo URL limits the accessibility of the method for further validation by the research community.
Limitations
While the DriftSE framework shows promising results, its reliance on a pre-trained self-supervised learning encoder may introduce limitations related to the quality and representativeness of the latent features. Additionally, the performance drop in unpaired settings suggests that the model may struggle in scenarios where clean-reference data is not available, highlighting a potential area for improvement.
Broader impact
The DriftSE framework has significant implications for real-time speech enhancement applications, particularly in environments with varying noise conditions. Its ability to perform one-step inference could facilitate deployment in low-latency scenarios, such as telecommunication and assistive technologies. Furthermore, the methodology could inspire future research in generative modeling and distribution matching across other domains beyond audio. The main contribution of this paper is the introduction of DriftSE, a novel generative framework for speech enhancement that reformulates denoising as an equilibrium problem, achieving high-fidelity results in a single inference step. This work represents a significant advancement in the field of speech enhancement, combining innovative methodology with robust experimental validation to address critical challenges in real-time applications.
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