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Paper detail

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

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.

preprint2022arXivOpen access
M. Iftekhar TanveerDiego CasabuenaJussi KarlgrenRosie Jones
Computation and Language
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M. Iftekhar TanveerDiego CasabuenaJussi KarlgrenRosie Jones

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