Human activity recognition (HAR) has been of interest in recent years due to the growing
demands in many areas. Applications of HAR include healthcare systems to monitor …

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in
parallelizing their state computations. In this work, we propose the Simple Recurrent Unit …

In the last few years, an emerging trend in automatic speech recognition research is the
study of end-to-end (E2E) systems. Connectionist Temporal Classification (CTC), Attention …

Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E)
models for automatic speech recognition. Currently, there are three promising E2E methods …

Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very
promising end-to-end (E2E) model that may replace the popular hybrid model for automatic …

With the emergence of deep learning, the performance of automatic speech recognition
(ASR) systems has remarkably improved. Especially for resource-rich languages such as …

Common recurrent neural network architectures scale poorly due to the intrinsic difficulty in
parallelizing their state computations. In this work, we propose the Simple Recurrent Unit …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

High accuracy speech recognition requires a large amount of transcribed data for
supervised training. In the absence of such data, domain adaptation of a well-trained …

The behavior of deep neural networks (DNNs) is hard to understand. This makes it
necessary to explore post hoc explanation methods. We conduct the first comprehensive …