  The Multi-modal Information based Speech Processing (MISP) challenge aims to
extend the application of signal processing technology in specific scenarios by
promoting the research into wake-up words, speaker diarization, speech
recognition, 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 and
recognition (AVDR) task that focuses on addressing ``who spoken what when''
with audio-visual speaker diarization results. Both tracks focus on the Chinese
language, and use far-field audio and video in real home-tv scenarios: 2-6
people communicating each other with TV noise in the background. This paper
introduces the dataset, track settings, and baselines of the MISP2022
challenge. Our analyses of experiments and examples indicate the good
performance of AVDR baseline system, and the potential difficulties in this
challenge due to, e.g., the far-field video quality, the presence of TV noise
in the background, and the indistinguishable speakers.
