MCE 2018: The 1st Multi-target Speaker Detection and Identification Challenge Evaluation
This addresses the need for improved speaker verification in security and call center applications, but is incremental as it focuses on benchmarking existing methods.
The paper tackled the problem of multi-target speaker detection and identification in real-world telephone conversations, establishing a challenge to evaluate current speech technology's accuracy in detecting blacklisted speakers and identifying them, with baseline results provided.
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.