  Recently, the deep-belief-networks (DBN) based voice activity detection (VAD)
has been proposed. It is powerful in fusing the advantages of multiple
features, and achieves the state-of-the-art performance. However, the deep
layers of the DBN-based VAD do not show an apparent superiority to the
shallower layers. In this paper, we propose a denoising-deep-neural-network
(DDNN) based VAD to address the aforementioned problem. Specifically, we
pre-train a deep neural network in a special unsupervised denoising greedy
layer-wise mode, and then fine-tune the whole network in a supervised way by
the common back-propagation algorithm. In the pre-training phase, we take the
noisy speech signals as the visible layer and try to extract a new feature that
minimizes the reconstruction cross-entropy loss between the noisy speech
signals and its corresponding clean speech signals. Experimental results show
that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but
also shows an apparent performance improvement of the deep layers over
shallower layers.

Machine Learning;Sound;Machine Learning