论文标题
Voicemos挑战的Sillwood Technologies系统2022
The Sillwood Technologies System for the VoiceMOS Challenge 2022
论文作者
论文摘要
在本文中，我们描述了我们为2022年Voicemos Challenge挑战的参赛作品，用于竞争的主要和室外（OOD）。我们的系统基于预先训练的预训练的自我监督的波形预测模型，同时通过平均随机重量提高了其概括能力。此外，我们将影响功能在训练集中可能的低质量数据中使用，以进一步提高模型的OOD轨迹性能。我们的系统分别在主要轨道和OOD轨道上排名第五和第七。
In this paper we describe our entry for the VoiceMOS Challenge 2022 for both the main and out-of-domain (OOD) track of the competition. Our system is based on finetuning pre-trained self-supervised waveform prediction models, while improving its generalisation ability through stochastic weight averaging. Further, we use influence functions to identity possible low-quality data within the training set to further increase our model's performance for the OOD track. Our system ranked 5th and joint 7th for the main track and OOD track, respectively.
