  This paper details our speaker diarization system designed for multi-domain,
multi-microphone casual conversations. The proposed diarization pipeline uses
weighted prediction error (WPE)-based dereverberation as a front end, then
applies end-to-end neural diarization with vector clustering (EEND-VC) to each
channel separately. It integrates the diarization result obtained from each
channel using diarization output voting error reduction plus overlap
(DOVER-LAP). To harness the knowledge from the target domain and results
integrated across all channels, we apply self-supervised adaptation for each
session by retraining the EEND-VC with pseudo-labels derived from DOVER-LAP.
The proposed system was incorporated into NTT's submission for the distant
automatic speech recognition task in the CHiME-7 challenge. Our system achieved
65 % and 62 % relative improvements on development and eval sets compared to
the organizer-provided VC-based baseline diarization system, securing third
place in diarization performance.
