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

The Multi-modal Information based Speech Processing (MISP) challenge aims toextend the application of signal processing technology in specific scenarios bypromoting the research into wake-up words, speaker diarization, speechrecognition, and other technologies. The MISP2022 challenge has two tracks: 1)audio-visual speaker diarization (AVSD), aiming to solve ``who spoken when''using both audio and visual data; 2) a novel audio-visual diarization andrecognition (AVDR) task that focuses on addressing ``who spoken what when''with audio-visual speaker diarization results. Both tracks focus on the Chineselanguage, and use far-field audio and video in real home-tv scenarios: 2-6people communicating each other with TV noise in the background. This paperintroduces the dataset, track settings, and baselines of the MISP2022challenge. Our analyses of experiments and examples indicate the goodperformance of AVDR baseline system, and the potential difficulties in thischallenge due to, e.g., the far-field video quality, the presence of TV noisein the background, and the indistinguishable speakers.

The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition