Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free
Md Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones · Interspeech 2022 · 2022
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.