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

arXiv: https://arxiv.org/abs/2303.06326 · HTML: https://mlsanity.com/paper/2303.06326/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition](https://mlsanity.com/paper/2403.04245/) (2024)
- [MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition](https://mlsanity.com/paper/2401.03424/) (2024)
- [Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder](https://mlsanity.com/paper/2404.05466/) (2024)
- [The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition](https://mlsanity.com/paper/2505.13971/) (2025)
- [From Speech to Interaction: Analyzing Multimodal Systems in Cocktail-Party Scenarios](https://mlsanity.com/paper/2608.08510/) (2026)

## Abstract (arXiv)

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.