cs.SD · 2026-10-01 · No. 130

Sound, 2026-10-01.

3 new papers in cs.SD. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion

    Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, Yuto Kondo

    cs.SD · cs.AI · cs.LG · eess.AS

    Flow-matching approaches to voice conversion (VC) have gained attention owing to their high speech quality and strong speaker similarity. Among them, one-step models such as MeanVoiceFlow are particularly attractive because they enable efficient inference; however, their reliance on a computationally intensive content encoder remains a bottleneck. We therefore propose MeanVoiceFlow2, a framework that jointly optimizes a flow-based conversion...

    arxiv.org/abs/2609.40087 · PDF

  2. 02

    SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models

    Rong Wan, Suliu Qin, Jiaxi Li, Wei Xie, Wenwu Wang, Xiaolong Han, Lu Yin, Xilu Wang

    cs.SD · cs.LG

    Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plausibility without verifying the underlying acoustic evidence. To address this issue, we first introduce spoofing evidence-grounded audio reasoning (SEAR), a four-task AQA benchmark to evaluate ALM-based ADD through acoustic evidence identification and quantification, deepfake detection, and...

    arxiv.org/abs/2609.39847 · PDF

  3. 03

    SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision

    Rong Wan, Wei Xie, Jiaxi Li, Wenwu Wang, Lu Yin, Yiliao Song, Xilu Wang

    cs.SD · cs.LG

    Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD methods are built on predefined supervision from ground-truth labels or verified forensic rationales. However, this paradigm overlooks an ALM's own mistakes, which indicate where targeted supervision is most needed. To this end, we first introduce evolving spoofing environments for ALM-based...

    arxiv.org/abs/2609.39679 · PDF

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