1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.SD2023★ 1 cited
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Joseph Konan, Ojas Bhargave, Shikhar Agnihotri +12
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (V…
cs.SD2023
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement
Muqiao Yang, Joseph Konan, David Bick +5
Despite rapid advancement in recent years, current speech enhancement models often produce speech that differs in perceptual quality from real clean speech. We propose a learning o…
cs.CL2023★ 1 cited
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
Yunyang Zeng, Joseph Konan, Shuo Han +5
Speech enhancement models have greatly progressed in recent years, but still show limits in perceptual quality of their speech outputs. We propose an objective for perceptual quali…