In this paper, we propose the use of self-supervised pretraining on a large
unlabelled data set to improve the performance of a personalized voice activity
detection (VAD) model in adverse conditions. We pretrain a long short-term
memory (LSTM)-encoder using the autoregressive predictive coding (APC)
framework and fine-tune it for personalized VAD. We also propose a denoising
variant of APC, with the goal of improving the robustness of personalized VAD.
The trained models are systematically evaluated on both clean speech and speech
contaminated by various types of noise at different SNR-levels and compared to
a purely supervised model. Our experiments show that self-supervised
pretraining not only improves performance in clean conditions, but also yields
models which are more robust to adverse conditions compared to purely
supervised learning.

本文提出了使用大规模无标签数据集上的自监督预训练，以改善个性化语音活动检测 (VAD) 模型在恶劣条件下的性能。我们使用自回归预测编码 (APC) 框架对长短期记忆 (LSTM) 编码器进行预训练，并进行个性化 VAD 的微调。我们还提出了一种去噪的 APC 变体，旨在提高个性化 VAD 的鲁棒性。经过系统评估后，我们发现在清晰环境下，自监督预训练不仅提高了性能，而且生成的模型与纯监督学习相比在恶劣条件下更加鲁棒。