Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Abstract
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 (VoIP) applications. Our approach involves adapting the DNS 2020 models to the specific acoustic characteristics of VoIP communications, which includes distortion and artifacts caused by compression, transmission, and platform-specific processing. To this end, we propose a multi-task learning framework for VoIP-DNS that jointly optimizes noise suppression and VoIP-specific acoustics for speech enhancement. We evaluate our approach on a diverse VoIP scenarios and show that it outperforms both industry performance and state-of-the-art methods for speech enhancement on VoIP applications. Our results demonstrate the potential of models trained on DNS-2020 to be improved and tailored to different VoIP platforms using VoIP-DNS, whose findings have important applications in areas such as speech recognition, voice assistants, and telecommunication.
Cite
@article{arxiv.2303.09048,
title = {Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms},
author = {Joseph Konan and Ojas Bhargave and Shikhar Agnihotri and Hojeong Lee and Ankit Shah and Shuo Han and Yunyang Zeng and Amanda Shu and Haohui Liu and Xuankai Chang and Hamza Khalid and Minseon Gwak and Kawon Lee and Minjeong Kim and Bhiksha Raj},
journal= {arXiv preprint arXiv:2303.09048},
year = {2023}
}
Comments
Under review at European Association for Signal Processing. 5 pages