  Podcasts are conversational in nature and speaker changes are frequent --
requiring speaker diarization for content understanding. We propose an
unsupervised technique for speaker diarization without relying on
language-specific components. The algorithm is overlap-aware and does not
require information about the number of speakers. Our approach shows 79%
improvement on purity scores (34% on F-score) against the Google Cloud Platform
solution on podcast data.
