Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Our experience of the world is multimodal-we see objects, hear sounds, feel texture, smell
odors, and taste flavors. Modality refers to the way in which something happens or is …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

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 …

Abstract We present Masked Audio-Video Learners (MAViL) to learn audio-visual
representations with three complementary forms of self-supervision:(1) reconstructing …

Lip-reading has attracted a lot of research attention lately thanks to advances in deep
learning. The current state-of-the-art model for recognition of isolated words in-the-wild …

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 Audiovisual SlowFast Networks, an architecture for integrated audiovisual
perception. AVSlowFast has Slow and Fast visual pathways that are deeply integrated with a …

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 …