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).
Abstract (translated)
在目标说话人提取（TSE）中，我们的目标是从多说话人的混合信号中恢复出目标语音，并使用短的注册语句作为参考。最近关于扩散和流匹配生成器的研究提高了目标语音的质量。然而，多步骤采样增加了延迟，而一步解决方案通常依赖于一个对现实对话可能不可靠的混合时间坐标。我们提出了AlphaFlowTSE，这是一个通过无雅可比向量积（JVP）自由的AlphaFlow目标进行训练的一步式条件生成模型。AlphaFlowTSE从观察到的混合信号开始学习均值速度传输沿混合至目标轨迹，消除了辅助混音比率预测，并结合流匹配与区间一致性师生目标来稳定训练。在Libri2Mix和REAL-T上的实验表明，AlphaFlowTSE提高了目标说话人的相似度以及对下游自动语音识别（ASR）的真实混合泛化能力。
URL
https://arxiv.org/abs/2603.10701
