Abstract

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 …

Date

March 1, 2022

Authors

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

Source

Computer Speech & Language

Volume

72

Pages

101317

Publisher

Academic Press