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 …

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 …

Improving the noise robustness of automatic speech recognition systems has been a
challenging task for many years. Recently, it was found that Deep Neural Networks (DNNs) …

Deep Neural Networks (DNNs) have been successfully applied to various speech tasks
during recent years. In this paper, we investigate the use of DNNs for noise-robust speech …

Noise compensation techniques for robust automatic speech recognition (ASR) attempt to
improve system performance in the presence of acoustic interference. In feature-based …

Deep neural networks (DNNs) are capable of modeling large acoustic variations. However,
the performance on noisy data is still below humans' expectations. In this work, we present …

It is difficult to apply well-formulated model-based noise adaptation approaches to Deep
Neural Network (DNN) due to the lack of interpretability of the model parameters. In this …

From prehistory to the multimedia digital age, speech communication has been the
dominant mode of human social bonding and information exchange. With the advancement …

In this paper, we propose a novel architecture for a deep learning system, named k-degree
layer-wise network, to realize efficient geo-distributed computing between Cloud and …

In this paper, we propose a novel architecture for a deep learning system, named k-degree
layer-wise network, to realize efficient geo-distributed computing between Cloud and …