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arXiv · 2603.10701

AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow

Duojia Li·Shuhan Zhang·Zihan Qian·Wenxuan Wu·Shuai Wang·Qingyang Hong·Lin Li·Haizhou Li
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Abstract

In target speaker extraction (TSE), we aim to recover target speech from a multi-talker mixture using a short enrollment utterance as reference. Recent studies on diffusion and flow-matching generators have improved target-speech fidelity. However, multi-step sampling increases latency, and one-step solutions often rely on a mixture-dependent time coordinate that can be unreliable for real-world conversations. We present AlphaFlowTSE, a one-step conditional generative model trained with a Jacobian-vector product (JVP)-free AlphaFlow objective. AlphaFlowTSE learns mean-velocity transport along a mixture-to-target trajectory starting from the observed mixture, eliminating auxiliary mixing-ratio prediction, and stabilizes training by combining flow matching with an interval-consistency teacher-student target. Experiments on Libri2Mix and REAL-T confirm that AlphaFlowTSE improves target-speaker similarity and real-mixture generalization for downstream automatic speech recognition (ASR).

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Duojia Li, Shuhan Zhang, Zihan Qian, Wenxuan Wu, Shuai Wang, Qingyang Hong, Lin Li, Haizhou Li. 2026-03-11. AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow. https://arxiv.org/abs/2603.10701

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