DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs

Desh Raj, Leibny Paola Garcia-Perera, Zili Huang, Shinji Watanabe, Daniel Povey, Andreas Stolcke, Sanjeev Khudanpur

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques, we propose an algorithm for combining outputs from such diarization systems through majority voting. Our method, DOVER-Lap, is inspired from the recently proposed DOVER algorithm, but is designed to handle overlapping segments in diarization outputs. We also modify the pair-wise incremental label mapping strategy used in DOVER, and propose an approximation algorithm based on weighted k-partite graph matching, which performs this mapping using a global cost tensor. We demonstrate the strength of our method by combining outputs from diverse systems - clustering-based, region proposal networks, and target-speaker voice activity detection - on AMI and LibriCSS datasets, where it consistently outperforms the single best system. Additionally, we show that DOVER-Lap can be used for late fusion in multichannel diarization, and compares favorably with early fusion methods like beamforming.

本文言語English
ホスト出版物のタイトル2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ881-888
ページ数8
ISBN(電子版)9781728170664
DOI
出版ステータスPublished - 2021 1 19
外部発表はい
イベント2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
継続期間: 2021 1 192021 1 22

出版物シリーズ

名前2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Conference

Conference2021 IEEE Spoken Language Technology Workshop, SLT 2021
国/地域China
CityVirtual, Shenzhen
Period21/1/1921/1/22

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ

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