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

2022-10-26 12:01:24
Bowen Pang, Huan Zhao, Gaosheng Zhang, Xiaoyue Yang, Yang Sun, Li Zhang, Qing Wang, Lei Xie
arXiv_AI
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.14653

PDF

https://arxiv.org/pdf/2210.14653.pdf

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