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The First Voice Timbre Attribute Detection Challenge

arXiv 2509.06635 ↗First posted 8 Sep 2025cs.SD cs.AIcited by 0

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Abstract (arXiv)

The first voice timbre attribute detection challenge is featured in a special session at NCMMSC 2025. It focuses on the explainability of voice timbre and compares the intensity of two speech utterances in a specified timbre descriptor dimension. The evaluation was conducted on the VCTK-RVA dataset. Participants developed their systems and submitted their outputs to the organizer, who evaluated the performance and sent feedback to them. Six teams submitted their outputs, with five providing descriptions of their methodologies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 8 Sep 2025.

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References · 8

  1. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  2. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  3. 2024NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Modelscited by 226
  4. 2021Simple Attention Module based Speaker Verification with Iterative noisy label detectioncited by 14
  5. 2024Explainable Attribute-Based Speaker Verificationcited by 11
  6. 2024Voice Attribute Editing with Text Promptcited by 9
  7. 2025The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plancited by 2
  8. 2025Introducing voice timbre attribute detectioncited by 1

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