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

M. Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones
7/25/2022
cs.CL

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Abstract

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.

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Cite this paper

@article{tanveer2022unsupervised,
  title  = {Unsupervised Speaker Diarization that is Agnostic to Language, Overlap-Aware, and Tuning Free},
  author = {M. Iftekhar Tanveer and Diego Casabuena and Jussi Karlgren and Rosie Jones},
  year   = {2022},
  eprint = {2207.12504},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.12504v1}
}

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