Year: 2019
Type: conference-paper
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.
Source: Interspeech (USB)
Authors: Kong Aik Lee, Ville Hautamäki, Tomi H. Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Héctor Delgado, Massimiliano Todisco
Institutions: Institute for Infocomm Research, University of Eastern Finland, NEC (Japan), Academia Sinica, Laboratoire d’Informatique de l’Université du Mans, National University of Singapore, Laboratoire Informatique d'Avignon, Nanyang Technological University, Matériaux Ingénierie et Science, Chinese Academy of Agricultural Sciences, EURECOM
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2941320063