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arXiv · 2607.06088

Flow Matching-Based Speech Source Separation with Best-of-N Biometric Sampling

Anastasia Zorkina·Alexandr Anikin·Nikita Khmelev·Anastasiya Korenevskaya·Sergey Novoselov·Vladimir Volokhov·Maxim Korenevsky·Yuriy Matveev
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Abstract

Single-channel speech separation remains challenging for real-world deployment due to source permutation ambiguity, sampling variability of generative models, and the difficulty of processing long recordings with chunk-wise inference. We address these issues with a conditional flow-matching-based method that produces an ordered two-source output conditioned on the mixture. A frozen speaker encoder defines the source order during training and is reused at inference for biometric best-of-$N$ candidate selection and chunk-level channel alignment. We evaluate separation quality on Libri2Mix benchmark using SI-SDR, PESQ, and ESTOI, and measure downstream impact using cpWER for automatic speech recognition and EER for speaker verification. The results show that the proposed Transformer U-Net variant is competitive with strong baselines in objective separation metrics and achieves the lowest downstream automatic speech recognition and speaker verification error rates in all evaluated settings.

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Anastasia Zorkina, Alexandr Anikin, Nikita Khmelev, Anastasiya Korenevskaya, Sergey Novoselov, Vladimir Volokhov, Maxim Korenevsky, Yuriy Matveev. 2026-07-07. Flow Matching-Based Speech Source Separation with Best-of-N Biometric Sampling. https://arxiv.org/abs/2607.06088

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