Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

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 …

In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural
networks by introducing gated direct connections between memory cells in adjacent layers …

Multichannel automatic speech recognition (ASR) systems commonly separate speech
enhancement, including localization, beamforming, and postfiltering, from acoustic …

Current distributed representations of words show little resemblance to theories of lexical
semantics. The former are dense and uninterpretable, the latter largely based on familiar …

We present a new approach to scalable training of deep learning machines by incremental
block training with intra-block parallel optimization to leverage data parallelism and …

Recently neural network acoustic models trained with Connectionist Temporal Classification
(CTC) were proposed as an alternative approach to conventional cross-entropy trained …

Joint multichannel enhancement and acoustic modeling using neural networks has shown
promise over the past few years. However, one shortcoming of previous work [1, 2, 3] is that …