Beyond U-Net: A Latent-Representation-Aligned Skip-Free Backbone for Flow-Matching Speech Enhancement
arXiv:2606.24745v1 Announce Type: cross Abstract: Generative models, particularly diffusion and score-based approaches, have recently achieved strong performance in speech enhancement, but their iterative sampling process limits real-time deployment. Flow Matching offers an efficient alternative by transporting noisy speech toward clean speech through an ordinary differential equation with few function evaluations. In this work, we propose a skip-free encoder-decoder backbone for flow-matching speech enhancement, guided by Latent Representation Alignment (LRA). Instead of relying on U-Net skip
The continuous push for real-time AI applications and more efficient generative models is driving innovations in fields like speech enhancement, seeking to overcome limitations of existing iterative methods.
This development proposes a significant improvement in the efficiency and real-world applicability of generative AI for speech enhancement by reducing computational requirements and latency.
The proposed skip-free backbone combined with Flow Matching could enable faster, more resource-efficient speech enhancement, moving it closer to real-time deployment in various applications.
- · AI compute providers
- · Real-time audio processing
- · Developers of generative AI applications
- · Speech technology companies
- · Latency-prone iterative generative models
Improved performance and broader adoption of real-time speech enhancement in devices and services.
Reduced computational costs for deploying high-quality generative AI in audio applications, potentially democratizing access.
Enhanced user experience in AI-powered communication and entertainment, fostering new audio interaction paradigms.
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Read at arXiv cs.AI