Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free

07/25/2022

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by   M. Iftekhar Tanveer, et al.

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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 solution on podcast data.