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Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020

arXiv 2010.11458 ↗First posted 22 Oct 2020eess.AS cs.SDcited by 17

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Abstract (arXiv)

This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recognition Challenge(VoxSRC) 2020. We will first explain our system design to address issues in handling real multi-talker recordings. We then present the details of the components, which include Res2Net-based speaker embedding extractor, conformer-based continuous speech separation with leakage filtering, and a modified DOVER (short for Diarization Output Voting Error Reduction) method for system fusion. We evaluate the systems with the data set provided by VoxSRCchallenge 2020, which contains real-life multi-talker audio collected from YouTube. Our best system achieves 3.71% and 6.23% of the diarization error rate (DER) on development set and evaluation set, respectively, being ranked the 1st at the diarization track of the challenge.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 2; 2024: 2.

17 papers.

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  1. 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 122
  2. 2021AVA-AVD: Audio-Visual Speaker Diarization in the Wildcited by 24
  3. 2021Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASRcited by 22
  4. 2022Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddingscited by 14
  5. 2021A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audiocited by 10
  6. 2024The VoxCeleb Speaker Recognition Challenge: A Retrospectivecited by 9
  7. 2021The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lapcited by 8
  8. 2022Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractorscited by 7
  9. 2022Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarizationcited by 7
  10. 2021Multi-scale speaker embedding-based graph attention networks for speaker diarisationcited by 6
  11. 2022In search of strong embedding extractors for speaker diarisationcited by 5
  12. 2022Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separationcited by 4
5 more
  1. 2024Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applicationscited by 3
  2. 2022Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challengecited by 2
  3. 2023End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversationscited by 2
  4. 2023SVVAD: Personal Voice Activity Detection for Speaker Verificationcited by 2
  5. 2022TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challengecited by 1