Transfer Learning for Voice Activity Detection: A Denoising Deep Neural Network Perspective
This addresses a practical issue in voice activity detection for noisy environments, but it is incremental as it applies existing transfer techniques to a specific domain.
The paper tackled the mismatch problem between source and target noisy corpora in voice activity detection by applying transfer learning with a denoising deep neural network, demonstrating effectiveness in experimental results.
Mismatching problem between the source and target noisy corpora severely hinder the practical use of the machine-learning-based voice activity detection (VAD). In this paper, we try to address this problem in the transfer learning prospective. Transfer learning tries to find a common learning machine or a common feature subspace that is shared by both the source corpus and the target corpus. The denoising deep neural network is used as the learning machine. Three transfer techniques, which aim to learn common feature representations, are used for analysis. Experimental results demonstrate the effectiveness of the transfer learning schemes on the mismatch problem.