Unsupervised domain adaptation for deep neural network based voice activity detection
Xiao-Lei Zhang · 2014
The mismatching problem between the training and test speech corpora hinders the practical use of the machine-learning-based voice activity detection (VAD). In this paper, we try to address this problem by the unsupervised domain adaptation techniques, which try to find a shared feature subspace between the mismatching corpora. The denoising deep neural network is used as the learning machine. Three domain adaptation techniques are used for analysis. Experimental results show that the unsupervised domain adaptation technique is promising to the mismatching problem of VAD.