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

A Thousand Words are Worth More Than One Recording: NLP Based Speaker Change Point Detection