4 citations · 4 across the 11 of their papers we have counts for
4 papers · 1 filter
PhASE-Flow: Phonetic-Conditioned Acoustic Flow Matching in SSL Representation Domain for Speech Enhancement
Jun Gao, Xiaobin Rong, Yu Sun +2
Flow matching (FM) enables high-fidelity generation, while self-supervised learning (SSL) speech models provide hierarchical representations spanning acoustic and phonetic levels.…
UniPASE: A Generative Model for Universal Speech Enhancement with High Fidelity and Low Hallucinations
Xiaobin Rong, Zheng Wang, Yushi Wang +2
Universal speech enhancement (USE) aims to restore speech signals from diverse distortions across multiple sampling rates. We propose UniPASE, an extension of the low-hallucination…
Rethinking Flow and Diffusion Bridge Models for Speech Enhancement
Dahan Wang, Jun Gao, Tong Lei +4
Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech si…
SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding
Bingsong Bai, Qihang Lu, Wenbing Yang +8
Paralinguistic sounds, like laughter and sighs, are crucial for synthesizing more realistic and engaging speech. However, existing methods typically depend on proprietary datasets,…