GAP-URGENET: A GENERATIVE-PREDICTIVE FUSION FRAMEWORK FOR UNIVERSAL SPEECH ENHANCEMENT

Xiaobin Rong

ICASSP 2026poster0 citations

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 https://xiaobin-rong.github.io/gap-urgenet_demo.

BibTeX

@inproceedings{icassp2026_gapurgenetagener,
  title = {GAP-URGENET: A GENERATIVE-PREDICTIVE FUSION FRAMEWORK FOR UNIVERSAL SPEECH ENHANCEMENT},
  author = {Xiaobin Rong},
  booktitle = {ICASSP 2026},
  year = {2026}
}