Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions
This work addresses robustness in personalized VAD for applications like speech processing, but it is incremental as it builds on existing self-supervised methods.
The paper tackled the problem of improving personalized voice activity detection (VAD) in adverse conditions by using self-supervised pretraining on unlabeled data, resulting in models that outperformed purely supervised approaches in both clean and noisy scenarios.
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.