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Active correction for speaker diarization with human in the loop

Abstract : State of the art diarization systems now achieve decent performance but those performances are often not good enough to deploy them without any human supervision. In this paper we propose a framework that solicits a human in the loop to correct the clustering by answering simple questions. After defining the nature of the questions, we propose an algorithm to list those questions and two stopping criteria that are necessary to limit the work load on the human in the loop. Experiments performed on the ALLIES dataset show that a limited interaction with a human expert can lead to considerable improvement of up to 36.5% relative diarization error rate (DER) compared to a strong baseline.
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https://hal.archives-ouvertes.fr/hal-03262923
Contributor : Anthony Larcher Connect in order to contact the contributor
Submitted on : Wednesday, June 16, 2021 - 6:10:33 PM
Last modification on : Friday, July 9, 2021 - 4:44:47 AM
Long-term archiving on: : Friday, September 17, 2021 - 7:26:22 PM

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  • HAL Id : hal-03262923, version 1

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Yevhenii Prokopalo, Meysam Shamsi, Loïc Barrault, Sylvain Meignier, Anthony Larcher. Active correction for speaker diarization with human in the loop. Iberspeech, Mar 2021, Valladolid, Spain. ⟨hal-03262923⟩

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