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V-Align is proposed, a structured framework that formulates VFA as an optimal mono-tonic path traversal problem over a frame–phoneme compatibility lattice and learns a soft traversal posterior and recovers discrete boundaries via dynamic programming.
A conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition learns an unconditional joint score network and enforces conditioning at inference via a plug-in correction term, and derives explicit conditional reverse-time SDEs and approximate probability-flow ODEs.
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