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Qinwen Hu

2 accepted papers

2026

PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement

AAAI 2026technical

Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of hallucination under severe noise, leading to incorrect spoken conten

Cited by 0SourcePDFScholar
2023

Convolutional Recurrent MetriCGAN With Spectral Dimension Compression For Full-Band Speech Enhancement

ICASSP 2023accepted

MetricGAN and its variations have been proven to be an effective wide-band speech enhancement model. In this paper, we expand it to full-band enhancement by combining our recently proposed learnable spectral dimension compression mapping strategy. The encoder-decoder structure with a time-frequency…

Cited by 0SourceScholar