MCE 2018: The 1st Multi-Target Speaker Detection and Identification Challenge Evaluation

Suwon Shon, Najim Dehak, Douglas Reynolds, James Glass


The Multi-target Challenge aims to assess how well current speech technology is able to determine whether or not a recorded utterance was spoken by one of a large number of blacklisted speakers. It is a form of multi-target speaker detection based on real-world telephone conversations. Data recordings are generated from call center customer-agent conversations. The task is to measure how accurately one can detect 1) whether a test recording is spoken by a blacklisted speaker, and 2) which specific blacklisted speaker was talking. This paper outlines the challenge and provides its baselines, results, and discussions.


 DOI: 10.21437/Interspeech.2019-1572

Cite as: Shon, S., Dehak, N., Reynolds, D., Glass, J. (2019) MCE 2018: The 1st Multi-Target Speaker Detection and Identification Challenge Evaluation. Proc. Interspeech 2019, 356-360, DOI: 10.21437/Interspeech.2019-1572.


@inproceedings{Shon2019,
  author={Suwon Shon and Najim Dehak and Douglas Reynolds and James Glass},
  title={{MCE 2018: The 1st Multi-Target Speaker Detection and Identification Challenge Evaluation}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={356--360},
  doi={10.21437/Interspeech.2019-1572},
  url={http://dx.doi.org/10.21437/Interspeech.2019-1572}
}