arXiv:2604.24199v3 Announce Type: replace-cross 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 u
The paper presents a novel generative framework for speech enhancement that moves towards more efficient one-step inference, indicating ongoing advancements in AI model architecture for audio processing.
Improved speech enhancement can significantly impact real-world AI applications by making speech interfaces more robust in noisy environments, enhancing communication, and enabling more accurate speech-to-text conversion.
Traditional iterative sampling for speech denoising is being challenged by more efficient one-step generative methods, potentially leading to faster and less computationally intensive audio processing.
- · AI speech processing companies
- · Voice assistant developers
- · Telecommunication industry
- · Hearing aid manufacturers
- · Companies reliant on older, less efficient denoising techniques
- · Generative models requiring extensive iterative sampling for audio
More accurate and faster real-time speech interaction in AI applications.
Reduced computational cost for deploying sophisticated audio enhancement features on edge devices.
New forms of user interfaces and services become viable as seamless speech interaction becomes ubiquitous, especially in challenging acoustic environments.
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Read at arXiv cs.AI