# The First Voice Timbre Attribute Detection Challenge

arXiv: https://arxiv.org/abs/2509.06635 · HTML: https://mlsanity.com/paper/2509.06635/

## 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.

## Builds on (most cited first, 8 of 8)

- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models](https://mlsanity.com/paper/2403.03100/) (2024)
- [Simple Attention Module based Speaker Verification with Iterative noisy label detection](https://mlsanity.com/paper/2110.06534/) (2021)
- [Explainable Attribute-Based Speaker Verification](https://mlsanity.com/paper/2405.19796/) (2024)
- [Voice Attribute Editing with Text Prompt](https://mlsanity.com/paper/2404.08857/) (2024)
- [The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan](https://mlsanity.com/paper/2505.09382/) (2025)
- [Introducing voice timbre attribute detection](https://mlsanity.com/paper/2505.09661/) (2025)

## 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.