In this paper, we present a survey on the application of recurrent neural networks to the task
of statistical language modeling. Although it has been shown that these models obtain good …

Over the past decade or so, several advances have been made to the design of modern
large vocabulary continuous speech recognition (LVCSR) systems to the point where their …

Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have
shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech …

Learning an acoustic model directly from the raw waveform has been an active area of
research. However, waveformbased models have not yet matched the performance of …

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 …

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 …

This paper investigates data augmentation for deep neural network acoustic modeling
based on label-preserving transformations to deal with data sparsity. Two data …

While Deep Neural Networks (DNNs) have achieved tremendous success for large
vocabulary continuous speech recognition (LVCSR) tasks, training of these networks is …

We describe how to effectively train neural network based language models on large data
sets. Fast convergence during training and better overall performance is observed when the …

We describe the latest improvements to the IBM English conversational telephone speech
recognition system. Some of the techniques that were found beneficial are: maxout networks …