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A Review of Common Online Speaker Diarization Methods

arXiv 2406.14464 ↗First posted 20 Jun 2024cs.SD cs.CL eess.AScited by 0

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Abstract (arXiv)

Speaker diarization provides the answer to the question "who spoke when?" for an audio file. This information can be used to complete audio transcripts for further processing steps. Most speaker diarization systems assume that the audio file is available as a whole. However, there are scenarios in which the speaker labels are needed immediately after the arrival of an audio segment. Speaker diarization with a correspondingly low latency is referred to as online speaker diarization. This paper provides an overview. First the history of online speaker diarization is briefly presented. Next a taxonomy and datasets for training and evaluation are given. In the sections that follow, online diarization methods and systems are discussed in detail. This paper concludes with the presentation of challenges that still need to be solved by future research in the field of online speaker diarization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 20 Jun 2024.

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19 of 49 references.

References · 19

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 122
  3. 2020Spot the conversation: speaker diarisation in the wildcited by 75
  4. 2017Speaker Diarization with LSTMcited by 73
  5. 2020Continuous speech separation: dataset and analysiscited by 73
  6. 2018Fully Supervised Speaker Diarizationcited by 46
  7. 2020The Third DIHARD Diarization Challengecited by 44
  8. 2019The Second DIHARD Diarization Challenge: Dataset, task, and baselinescited by 39
  9. 2021Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detectioncited by 31
  10. 2021Overlap-aware low-latency online speaker diarization based on end-to-end local segmentationcited by 14
  11. 2020Online End-to-End Neural Diarization with Speaker-Tracing Buffercited by 13
  12. 2020BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakerscited by 10
7 more
  1. 2020Speaker Diarization as a Fully Online Learning Problem in MiniVoxcited by 7
  2. 2022Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractorscited by 7
  3. 2022Low-Latency Speech Separation Guided Diarization for Telephone Conversationscited by 5
  4. 2019Supervised online diarization with sample mean loss for multi-domain datacited by 3
  5. 2021Low-Latency Online Speaker Diarization with Graph-Based Label Generationcited by 3
  6. 2023Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractorscited by 2
  7. 2023End-to-end Online Speaker Diarization with Target Speaker Trackingcited by 1

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