English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Speaker diarization is well studied for constrained audios but littleexplored for challenging in-the-wild videos, which have more speakers, shorterutterances, and inconsistent on-screen speakers. We address this gap byproposing an audio-visual diarization model which combines audio-only andvisual-centric sub-systems via late fusion. For audio, we show that anattractor-based end-to-end system (EEND-EDA) performs remarkably well whentrained with our proposed recipe of a simulated proxy dataset, and propose animproved version, EEND-EDA++, that uses attention in decoding and a speakerrecognition loss during training to better handle the larger number ofspeakers. The visual-centric sub-system leverages facial attributes andlip-audio synchrony for identity and speech activity estimation of on-screenspeakers. Both sub-systems surpass the state of the art (SOTA) by a largemargin, with the fused audio-visual system achieving a new SOTA on the AVA-AVDbenchmark.

Late Audio-Visual Fusion for In-The-Wild Speaker Diarization