  Speaker diarization has been investigated extensively as an important central
task for meeting analysis. Recent trend shows that integration of end-to-end
neural (EEND)-and clustering-based diarization is a promising approach to
handle realistic conversational data containing overlapped speech with an
arbitrarily large number of speakers, and achieved state-of-the-art results on
various tasks. However, the approaches proposed so far have not realized {\it
tight} integration yet, because the clustering employed therein was not optimal
in any sense for clustering the speaker embeddings estimated by the EEND
module. To address this problem, this paper introduces a {\it trainable}
clustering algorithm into the integration framework, by deep-unfolding a
non-parametric Bayesian model called the infinite Gaussian mixture model
(iGMM). Specifically, the speaker embeddings are optimized during training such
that it better fits iGMM clustering, based on a novel clustering loss based on
Adjusted Rand Index (ARI). Experimental results based on CALLHOME data show
that the proposed approach outperforms the conventional approach in terms of
diarization error rate (DER), especially by substantially reducing speaker
confusion errors, that indeed reflects the effectiveness of the proposed iGMM
integration.
