The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Human trajectory prediction is challenging and critical in various applications (eg,
autonomous vehicles and social robots). Because of the continuity and foresight of the …

In this paper, we propose an open source, production first, and production ready speech
recognition toolkit called WeNet in which a new two-pass approach is implemented to unify …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS),
subsume the acoustic, pronunciation and language model components of a traditional …

Recently, Transformer based end-to-end models have achieved great success in many
areas including speech recognition. However, compared to LSTM models, the heavy …

Artificial intelligence (AI) is the development of computer systems that are able to perform
tasks that normally require human intelligence. Advances in AI software and hardware …