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A Triplet Ranking-Based Neural Network for Speaker Diarization and Linking

Abstract : This paper investigates a novel neural scoring method, based on conventional i-vectors, to perform speaker diarization and linking of large collections of recordings. Using triplet loss for training, the network projects i-vectors in a space that better separates speakers in terms of cosine similarity. Experiments are run on two French TV collections built from REPERE [1] and ETAPE [2] campaigns corpora, the system being trained on French Radio data. Results indicate that the proposed approach outperforms conventional cosine and Probabilistic Linear Discriminant Analysis scoring methods on both within-and cross-recording diarization tasks, with a Diarization Error Rate reduction of 14% in average.
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https://hal.archives-ouvertes.fr/hal-01818401
Contributor : Anthony Larcher <>
Submitted on : Monday, November 19, 2018 - 9:26:45 AM
Last modification on : Friday, April 26, 2019 - 1:37:30 PM
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Gaël Le Lan, Delphine Charlet, Anthony Larcher, Sylvain Meignier. A Triplet Ranking-Based Neural Network for Speaker Diarization and Linking. Interspeech 2017, Aug 2017, Stockholm, Sweden. ⟨10.21437/Interspeech.2017-270⟩. ⟨hal-01818401⟩

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