Speaker diarization: An emerging research

Abstract

Speaker diarization is the task of determining “Who spoke when?”, where the objective is to annotate a continuous audio recording with appropriate speaker labels corresponding to the time regions where they spoke. The labels are not necessarily the actual speaker identities, i.e. speaker identification, as long as the same labels are assigned to the regions uttered by the same speakers. These regions may overlap as multiple speakers could talk simultaneously. Speaker diarization is thus essentially the combination of two different processes: segmentation, in which the speaker turns are detected, and unsupervised clustering, in which segments of the same speakers are grouped. The clustering process is considered as unsupervised problem since there is no prior information about the number of speakers, their identities or acoustic conditions (Meignier et al., Comput Speech Lang 20(2–3):303–330, 2006; Zhou and Hansen, IEEE Trans Speech Audio Process 13(4):467–474, 2005). This chapter presents the fundamentals of speaker diarization and the most significant works over the recent years on this topic.