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Electrical Engineering and Systems Science > Audio and Speech
Processing

arXiv:2505.14465 (eess)
[Submitted on 20 May 2025]

Title:FlowTSE: Target Speaker Extraction with Flow Matching

Authors:Aviv Navon, Aviv Shamsian, Yael Segal-Feldman, Neta Glazer,
Gil Hetz, Joseph Keshet
View a PDF of the paper titled FlowTSE: Target Speaker Extraction
with Flow Matching, by Aviv Navon and 5 other authors
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    Abstract:Target speaker extraction (TSE) aims to isolate a
    specific speaker's speech from a mixture using speaker enrollment
    as a reference. While most existing approaches are
    discriminative, recent generative methods for TSE achieve strong
    results. However, generative methods for TSE remain
    underexplored, with most existing approaches relying on complex
    pipelines and pretrained components, leading to computational
    overhead. In this work, we present FlowTSE, a simple yet
    effective TSE approach based on conditional flow matching. Our
    model receives an enrollment audio sample and a mixed speech
    signal, both represented as mel-spectrograms, with the objective
    of extracting the target speaker's clean speech. Furthermore, for
    tasks where phase reconstruction is crucial, we propose a novel
    vocoder conditioned on the complex STFT of the mixed signal,
    enabling improved phase estimation. Experimental results on
    standard TSE benchmarks show that FlowTSE matches or outperforms
    strong baselines.

Comments: InterSpeech 2025
Subjects: Audio and Speech Processing (eess.AS); Machine Learning
          (cs.LG)
Cite as:  arXiv:2505.14465 [eess.AS]
          (or arXiv:2505.14465v1 [eess.AS] for this version)
          https://doi.org/10.48550/arXiv.2505.14465
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Aviv Shamsian [view email]
[v1] Tue, 20 May 2025 15:01:30 UTC (1,402 KB)
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