GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement

Abstract:We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which performs full-stack speech restoration in a self-supervised representation domain and reconstructs the waveform via a neural vocoder, along with a predictive branch that performs spectrogram-domain enhancement, providing complementary cues. Outputs from both branches are fused by a post-processing module, which also performs bandwidth extension to generate the enhanced waveform at 48 kHz, later downsampled to the original sampling rate. This generative-predictive fusion improves robustness and perceptual quality, achieving top performance in the blind-test phase and ranking 1st in the objective evaluation. Audio examples are available at this https URL.

Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2604.01832 [eess.AS]

(or arXiv:2604.01832v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2604.01832 Focus to learn more arXiv-issued DOI via DataCite

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