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arXiv · 2611.08011

Route-DMD: Distribution Matching Distillation with Task-Routed Mechanism for Speech Foundation Models

Yanru Huo·Zhikang Niu·Wenming Tu·Junxi Liu·Tian Tan·Shan Yang·Yipeng Kang·Ziyang Ma·Xie Chen·Zilong Zheng
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Abstract

Speech foundation models built on flow matching handle generation, editing, and processing in one system, but every request costs 20-50 iterative steps, and classifier-free guidance (CFG) doubles each of them. Distribution matching distillation (DMD) trains a student that approaches the teacher's quality in a few steps, yet existing speech distillation work applies it to generation alone. We show that a distribution-matching objective does not serve every task: in separation, the noised states of erroneous student outputs deviate far from the teacher's training distribution, where its prediction is unreliable. Route-DMD therefore makes the objective task-dependent, routing the affected tasks to a regression loss and keeping decoupled distribution matching elsewhere. The student is initialized by consistency distillation, and teacher rollouts use adaptive projected guidance (APG) instead of CFG. Without CFG, the 4-step student matches the 32-step teacher on generation, editing, and processing tasks at a 4.5$\times$ end-to-end speedup.

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Yanru Huo, Zhikang Niu, Wenming Tu, Junxi Liu, Tian Tan, Shan Yang, Yipeng Kang, Ziyang Ma, Xie Chen, Zilong Zheng. 2026-11-05. Route-DMD: Distribution Matching Distillation with Task-Routed Mechanism for Speech Foundation Models. https://arxiv.org/abs/2611.08011

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