Speaker linking in large data sets

  title={Speaker linking in large data sets},
  author={David A. van Leeuwen},
This paper investigates the task of linking speakers across multiple recordings, which can be accomplished by speaker clustering. Various aspects are considered, such as computational complexity, on/offline approaches, and evaluation measures but also speaker recognition approaches. It has not been the aim of this study to optimize clustering performance, but as an experimental exercise, we perform speaker linking on all ‘1conv-4w’ conversation sides of the NIST-2006 evaluation data set. This… CONTINUE READING
Highly Cited
This paper has 45 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 34 extracted citations

Efficient iterative mean shift based cosine dissimilarity for multi-recording speaker clustering

2013 IEEE International Conference on Acoustics, Speech and Signal Processing • 2013
View 5 Excerpts
Highly Influenced

An Adaptive Method for Cross-Recording Speaker Diarization

IEEE/ACM Transactions on Audio, Speech, and Language Processing • 2018
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 17 references

Niko Brümmer and Edward de Villiers , “ The speaker partitioning problem , ” in Proc . Odyssey

Michael Steinbach Pang-Ning Tan
: The Speaker and Language Recognition Workshop , Brno , June 2010 , Submitted . [ 2 ] • 2010

and Marijn Huijbregts , “ The AMI speaker diarization system for NIST RT 06 s meeting data

David A. van Leeuwen
Machine Learning for Multimodal Interaction , vol . 4299 of Lecture Notes in Computer Science • 2007

, and Albert Strassheim , “ Fusion of heterogeneous speaker recognition systems in the STBU submission for the nist speaker recognition evaluation 2006

Niko Brümmer, Jan Černocký Lukáš Burget, +5 authors Petr Schwarz
IEEE Transactions on Speech , Audio and Language Processing • 2006

Pardo , “ Robust speaker diarization for meetings : Icsi rt 06 s meetings evaluation system

Xavier Anguera, Chuck Wooters, M Jose
Machine Learning for Multimodal Interaction , vol . 4299 of Lecture Notes in Computer Science • 2006

Similar Papers

Loading similar papers…