# Interspeech 2025 URGENT Speech Enhancement Challenge

arXiv: https://arxiv.org/abs/2505.23212 · HTML: https://mlsanity.com/paper/2505.23212/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2025.

## Built on by (most cited first, 12 of 12)

- [FlowSE-GRPO: Training Flow Matching Speech Enhancement via Online Reinforcement Learning](https://mlsanity.com/paper/2601.16483/) (2026)
- [UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement](https://mlsanity.com/paper/2510.20441/) (2025)
- [Testing the Testers: Human-Driven Quality Assessment of Voice AI Testing Platforms](https://mlsanity.com/paper/2511.04133/) (2025)
- [GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement](https://mlsanity.com/paper/2604.01832/) (2026)
- [On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation](https://mlsanity.com/paper/2508.18833/) (2025)
- [StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement](https://mlsanity.com/paper/2603.09234/) (2026)
- [Efficient Multi-View 3D Object Detection by Dynamic Token Selection and Fine-Tuning](https://mlsanity.com/paper/2604.13586/) (2026)
- [UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations](https://mlsanity.com/paper/2604.14606/) (2026)
- [TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation](https://mlsanity.com/paper/2606.29575/) (2026)
- [Rethinking Language Model-Based Generative Speech Enhancement in the Latent Space of a Neural Audio Codec](https://mlsanity.com/paper/2608.12082/) (2026)
- [StrixAE: An Intelligent Agent for Audio Enhancement under Complex Distortion Coupling in Real-World Scenarios](https://mlsanity.com/paper/2609.03414/) (2026)
- [SAM3-O2D2: Zero-Shot Object Out-of-Distribution Detection by Object Class Prompting of the SAM3-Image Model](https://mlsanity.com/paper/2609.08281/) (2026)

## Abstract (arXiv)

There has been a growing effort to develop universal speech enhancement (SE) to handle inputs with various speech distortions and recording conditions. The URGENT Challenge series aims to foster such universal SE by embracing a broad range of distortion types, increasing data diversity, and incorporating extensive evaluation metrics. This work introduces the Interspeech 2025 URGENT Challenge, the second edition of the series, to explore several aspects that have received limited attention so far: language dependency, universality for more distortion types, data scalability, and the effectiveness of using noisy training data. We received 32 submissions, where the best system uses a discriminative model, while most other competitive ones are hybrid methods. Analysis reveals some key findings: (i) some generative or hybrid approaches are preferred in subjective evaluations over the top discriminative model, and (ii) purely generative SE models can exhibit language dependency.