Objectives This paper provides an overview of existing applications and concepts of robotic
systems and artificial intelligence in dentistry. This review aims to provide the community …

Conventional automatic speech recognition (ASR) based on a hidden Markov model
(HMM)/deep neural network (DNN) is a very complicated system consisting of various …

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We
learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) …

Internet and social media platforms such as Twitter, Facebook, and several blogs provide
various types of helpful information worldwide. The increased usage of social media and e …

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 …

Recent work on end-to-end automatic speech recognition (ASR) has shown that the
connectionist temporal classification (CTC) loss can be used to convert acoustics to phone …

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received
increasing attention compared to conventional subword based automatic speech …

Highly performing deep neural networks come at the cost of computational complexity that
limits their practicality for deployment on portable devices. We propose the low-rank …

Techniques for multi-lingual and cross-lingual speech recognition can help in low resource
scenarios, to bootstrap systems and enable analysis of new languages and domains. End-to …

This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC method in …