  This report presents the system developed by the ABSP Laboratory team for the
third DIHARD speech diarization challenge. Our main contribution in this work
is to develop a simple and efficient solution for acoustic domain dependent
speech diarization. We explore speaker embeddings for \emph{acoustic domain
identification} (ADI) task. Our study reveals that i-vector based method
achieves considerably better performance than x-vector based approach in the
third DIHARD challenge dataset. Next, we integrate the ADI module with the
diarization framework. The performance substantially improved over that of the
baseline when we optimized the thresholds for agglomerative hierarchical
clustering and the parameters for dimensionality reduction during scoring for
individual acoustic domains. We 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.
