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.

本文提出了一种适用于 VoIP 应用的深度噪声抑制模型的微调方法，并通过使用多任务学习框架联合优化噪声抑制和特定于 VoIP 的声学，表明该方法优于行业绩效和最先进的语音增强方法，并展示了 DNS-2020 模型在不同 VoIP 平台上具有潜在优化的潜力，这些发现在语音识别、语音助手和通信等领域具有重要应用。