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

## TL;DR

This paper presents the first challenge evaluation for multi-target speaker detection and identification, focusing on real-world call center data to assess current speech technology's effectiveness in identifying blacklisted speakers.

## Contribution

It introduces a new benchmark challenge for multi-target speaker detection using real-world telephone conversation data, along with baseline results and analysis.

## Key findings

- Baseline systems achieved moderate detection accuracy.
- Identification of blacklisted speakers remains challenging in real-world scenarios.
- The challenge provides a standard for future research in speaker detection and identification.

## Abstract

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.