mlsanityData to 7 Oct 2026

TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge

arXiv 2210.14653 ↗First posted 26 Oct 2022cs.SD cs.AI eess.AScited by 1

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Abstract (arXiv)

This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 26 Oct 2022.

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31 of 42 references.

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19 more
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