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This paper describes the FlySpeech speaker diarization system submitted tothe second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech\textbf{P}rocessing~(\textbf{MISP}) Challenge held in ICASSP 2022. We developan end-to-end audio-visual speaker diarization~(AVSD) system, which consists ofa lip encoder, a speaker encoder, and an audio-visual decoder. Specifically, tomitigate the degradation of diarization performance caused by separatetraining, we jointly train the speaker encoder and the audio-visual decoder. Inaddition, we leverage the large-data pretrained speaker extractor to initializethe speaker encoder.

The FlySpeech Audio-Visual Speaker Diarization System for MISP Challenge 2022