Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

Audio-visual learning, aimed at exploiting the relationship between audio and visual
modalities, has drawn considerable attention since deep learning started to be used …

Video recordings of speech contain correlated audio and visual information, providing a
strong signal for speech representation learning from the speaker's lip movements and the …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

Visual speech recognition (VSR) aims to recognize the content of speech based on lip
movements, without relying on the audio stream. Advances in deep learning and the …

In this work, we present a hybrid CTC/Attention model based on a ResNet-18 and
Convolution-augmented transformer (Conformer), that can be trained in an end-to-end …

Audio-visual speech recognition has received a lot of attention due to its robustness against
acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech …

Audio-based automatic speech recognition (ASR) degrades significantly in noisy
environments and is particularly vulnerable to interfering speech, as the model cannot …

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 …

Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable
speech recognition, particularly when audio is corrupted by noise. Additional visual …