Language modeling studies the probability distributions over strings of texts. It is one of the
most fundamental tasks in natural language processing (NLP). It has been widely used in …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

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 …

The goal of this work is to recognise phrases and sentences being spoken by a talking face,
with or without the audio. Unlike previous works that have focussed on recognising a limited …

End-to-end (E2E) models, which directly predict output character sequences given input
speech, are good candidates for on-device speech recognition. E2E models, however …

Taking full advantage of the information from both vision and language is critical for the
video captioning task. Existing models lack adequate visual representation due to the …

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

The goal of this paper is to learn strong lip reading models that can recognise speech in
silent videos. Most prior works deal with the open-set visual speech recognition problem by …

We present ESPnet-ST, which is designed for the quick development of speech-to-speech
translation systems in a single framework. ESPnet-ST is a new project inside end-to-end …