  This paper describes our NPU-ASLP system for the Audio-Visual Diarization and
Recognition (AVDR) task in the Multi-modal Information based Speech Processing
(MISP) 2022 Challenge. Specifically, the weighted prediction error (WPE) and
guided source separation (GSS) techniques are used to reduce reverberation and
generate clean signals for each single speaker first. Then, we explore the
effectiveness of Branchformer and E-Branchformer based ASR systems. To better
make use of the visual modality, a cross-attention based multi-modal fusion
module is proposed, which explicitly learns the contextual relationship between
different modalities. Experiments show that our system achieves a concatenated
minimum-permutation character error rate (cpCER) of 28.13\% and 31.21\% on the
Dev and Eval set, and obtains second place in the challenge.
