  This report describes the speaker diarization system developed by the ABSP
Laboratory team for the third DIHARD speech diarization challenge. Our primary
contribution is to develop acoustic domain identification (ADI) system for
speaker diarization. We investigate speaker embeddings based ADI system. We
apply a domain-dependent threshold for agglomerative hierarchical clustering.
Besides, we optimize the parameters for PCA-based dimensionality reduction in a
domain-dependent way. Our method of integrating domain-based processing schemes
in the baseline system of the challenge achieved a relative improvement of
$9.63\%$ and $10.64\%$ in DER for core and full conditions, respectively, for
Track 1 of the DIHARD III evaluation set.
