Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

Active speaker detection is an important component in video analysis algorithms for
applications such as speaker diarization, video re-targeting for meetings, speech …

Current methods for active speaker detection focus on modeling audiovisual information
from a single speaker. This strategy can be adequate for addressing single-speaker …

Active speaker detection requires a solid integration of multi-modal cues. While individual
modalities can approximate a solution, accurate predictions can only be achieved by …

We present an automatic voice activity detection (VAD) method that is solely based on visual
cues. Unlike traditional approaches processing audio, we show that upper body motion …

Conventional audio-visual approaches for active speaker detection (ASD) typically rely on
visually pre-extracted face tracks and the corresponding single-channel audio to find the …

We investigated the mouth-opening transition pattern (MOTP), which represents the change
of mouth-opening degree during the end of an utterance, and used it to predict the next …

The goal of this work is to synchronise audio and video of a talking face using deep neural
network models. Existing works have trained networks on proxy tasks such as cross-modal …

We present a novel vision-based voice activity detection (VAD) method that relies only on
automatic upper body motion (UBM) analysis. Traditionally, VAD is performed using audio …

Meetings are a common activity in professional contexts, and it remains challenging to
endow vocal assistants with advanced functionalities to facilitate meeting management. In …