In this paper, we provide an overview of the invited and contributed papers presented at the
special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for …

In this invited paper, my overview material on the same topic as presented in the plenary
overview session of APSIPA-2011 and the tutorial material presented in the same …

We present a state-of-the-art speech recognition system developed using end-to-end deep
learning. Our architecture is significantly simpler than traditional speech systems, which rely …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

We show empirically that in SGD training of deep neural networks, one can, at no or nearly
no loss of accuracy, quantize the gradients aggressively—to but one bit per value—if the …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Deep neural network acoustic models produce substantial gains in large vocabulary
continuous speech recognition systems. Emerging work with rectified linear (ReL) hidden …

Abstract Convolutional Neural Networks (CNNs) are an alternative type of neural network
that can be used to reduce spectral variations and model spectral correlations which exist in …

Semantic slot filling is one of the most challenging problems in spoken language
understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for …

Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic
models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech …