arXiv:2606.29575v1 Announce Type: cross Abstract: Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment on edge devices. To address this, we propose TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost. Our method introduces dynamic expert specialization in time and frequency dimensions through alternating time-wise and frequency-wise MoE modules, each dynamically selecting experts per frame or mel ba
The continuous drive for more efficient AI on edge devices, coupled with advancements in MoE architectures, makes this an opportune time for innovations like TF-MoE.
This development could significantly reduce the computational cost of speech separation, making advanced AI audio processing more accessible and pervasive on edge devices.
The barrier for deploying sophisticated speech separation models on resource-constrained devices potentially lowers, enabling new applications and improving existing ones without relying on cloud computation.
- · Edge device manufacturers
- · Voice assistant developers
- · Telecommunications companies
- · AI hardware accelerators
More powerful and efficient speech AI processing becomes available directly on consumer and industrial edge devices.
Enhanced privacy for audio data as less information needs to be sent to the cloud for processing.
Accelerated development of real-time, context-aware audio interfaces for new form factors and environments.
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Read at arXiv cs.AI