Unsupervised Speaker Diarization that is Agnostic to Language Overlap Aware and Free of Tuning
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.