  Speaker Diarization (SD) consists of splitting or segmenting an input audio
burst according to speaker identities. In this paper, we focus on the crucial
task of the SD problem which is the audio segmenting process and suggest a
solution for the Change Point Detection (CPD) problem. We empirically
demonstrate the negative correlation between an increase in the number of
speakers and the Recall and F1-Score measurements. This negative correlation is
shown to be the outcome of a massive experimental evaluation process, which
accounts its superiority to recently developed voice based solutions. In order
to overcome the number of speakers issue, we suggest a robust solution based on
a novel Natural Language Processing (NLP) technique, as well as a metadata
features extraction process, rather than a vocal based alone. To the best of
our knowledge, we are the first to propose an intelligent NLP based solution
that (I) tackles the CPD problem with a dataset in Hebrew, and (II) solves the
CPD variant of the SD problem. We empirically show, based on two distinct
datasets, that our method is abled to accurately identify the CPDs in an audio
burst with 82.12% and 89.02% of success in the Recall and F1-score
measurements.
