ICASSP 2026 URGENT Speech Enhancement Challenge
🤖 AI Summary
This work proposes the first unified challenge framework that integrates general-purpose speech enhancement with speech quality assessment, aiming to address the robustness and generalization of systems under unknown distortion types, domains, and input conditions. By leveraging multi-source, multi-scenario training data, a standardized evaluation protocol, and baseline models, the framework enables fair comparisons between end-to-end deep learning approaches and traditional signal processing methods. The challenge features two complementary tracks: Track 1 focuses on general enhancement performance, while Track 2 incorporates objective quality assessment. The initiative attracted over 80 registered teams, with 29 submitting valid solutions, significantly advancing community-wide research and progress in general-purpose speech enhancement.
Application Category
📝 Abstract
The ICASSP 2026 URGENT Challenge advances the series by focusing on universal speech enhancement (SE) systems that handle diverse distortions, domains, and input conditions. This overview paper details the challenge's motivation, task definitions, datasets, baseline systems, evaluation protocols, and results. The challenge is divided into two complementary tracks. Track 1 focuses on universal speech enhancement, while Track 2 introduces speech quality assessment for enhanced speech. The challenge attracted over 80 team registrations, with 29 submitting valid entries, demonstrating significant community interest in robust SE technologies.
Problem
Research questions and friction points this paper is trying to address.
universal speech enhancement
speech quality assessment
diverse distortions
robust speech enhancement
Innovation
Methods, ideas, or system contributions that make the work stand out.
universal speech enhancement
robustness
speech quality assessment
multi-domain generalization
challenge benchmark
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