  In this paper two different approaches to enhance the performance of the most
challenging component of a Speaker Diarization system are presented, i.e. the
speaker clustering part. A processing step is proposed enhancing the input
features with a temporal smoothing process combined with nonlinear filtering.
We, also, propose improvements on the Deep Embedded Clustering (DEC) algorithm
-- a nonlinear feature transformation. The performance of these enhancements is
compared with different clustering algorithms, such as the UISRNN, k-Means,
Spectral clustering and x-Means. The evaluation is held on three different
tasks, i.e. the AMI, DIHARD and an internal meeting transcription task. The
proposed approaches assume a known number of speakers and time segmentations
for the audio files. Since, we focus only on the clustering component of
diarization for this work, the segmentation provided is assumed perfect.
Finally, we present how supervision, in the form of given speaker profiles, can
further improve the overall diarization performance. The proposed enhancements
yield substantial relative improvements in all 3 tasks, with 20\% in AMI and
19\% better than the best diarization system for DIHARD task, when the number
of speakers is known.
