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

The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge