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I4U SUbMission to NIST SRE 2018: Leveraging from a decade of shared experiences

  • Kong Aik Lee
  • , Ville Hautamäki
  • , Tomi Kinnunen
  • , Hitoshi Yamamoto
  • , Koji Okabe
  • , Ville Vestman
  • , Jing Huang
  • , Guohong Ding
  • , Hanwu Sun
  • , Anthony Larcher
  • , Rohan K. Das
  • , Haizhou Li
  • , Mickael Rouvier
  • , Pierre Michel Bousquet
  • , Wei Rao
  • , Qing Wang
  • , Chunlei Zhang
  • , Fahimeh Bahmaninezhad
  • , Hector Delgado
  • , Massimiliano Todisco
  • Q. Wang, L. Guo, T. Koshinaka, J. Zhang, K. Shinoda, T. N. Trong, Md Sahidullah, F. Lu, Y. Tang, M. Tu, K. K. Teh, H. D. Tran, K. K. George, I. Kukanov, F. Desnous, J. Yang, E. Yılmaz, L. Xu, J. F. Bonastre, C. Xu, Z. H. Lim, E. S. Chng, S. Ranjan, J. H.L. Hansen, J. Patino, N. Evans
  • NEC Corporation
  • University of Eastern Finland
  • JD AI Research and Platform
  • Agency for Science, Technology and Research, Singapore
  • Le Mans Université
  • National University of Singapore
  • Avignon Université
  • Nanyang Technological University
  • Northwestern Polytechnical University Xian
  • University of Texas at Dallas
  • EURECOM Ecole d'Ingénieur et Centre de Recherche en Sciences du Numérique

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.

Original languageEnglish
Pages (from-to)1497-1501
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2019-September
DOIs
StatePublished - 2019
Externally publishedYes
Event20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019 - Graz, Austria
Duration: 15 Sep 201919 Sep 2019

Keywords

  • Benchmark evaluation
  • Speaker recognition

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