A review of speaker diarization: Recent advances with deep learning

Tae Jin Park*, Naoyuki Kanda, Dimitrios Dimitriadis, Kyu J. Han, Shinji Watanabe, Shrikanth Narayanan

*この研究の対応する著者

研究成果: Article査読

8 被引用数 (Scopus)

抄録

Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify “who spoke when”. In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing. These algorithms also gained their own value as a standalone application over time to provide speaker-specific metainformation for downstream tasks such as audio retrieval. More recently, with the emergence of deep learning technology, which has driven revolutionary changes in research and practices across speech application domains, rapid advancements have been made for speaker diarization. In this paper, we review not only the historical development of speaker diarization technology but also the recent advancements in neural speaker diarization approaches. Furthermore, we discuss how speaker diarization systems have been integrated with speech recognition applications and how the recent surge of deep learning is leading the way of jointly modeling these two components to be complementary to each other. By considering such exciting technical trends, we believe that this paper is a valuable contribution to the community to provide a survey work by consolidating the recent developments with neural methods and thus facilitating further progress toward a more efficient speaker diarization.

本文言語English
論文番号101317
ジャーナルComputer Speech and Language
72
DOI
出版ステータスPublished - 2022 3月
外部発表はい

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • ソフトウェア
  • 人間とコンピュータの相互作用

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