  Knowledge-based authentication is crucial for task-oriented spoken dialogue
systems that offer personalised and privacy-focused services. Such systems
should be able to enrol (E), verify (V), and identify (I) new and recurring
users based on their personal information, e.g. postcode, name, and date of
birth. In this work, we formalise the three authentication tasks and their
evaluation protocols, and we present EVI, a challenging spoken multilingual
dataset with 5,506 dialogues in English, Polish, and French. Our proposed
models set the first competitive benchmarks, explore the challenges of
multilingual natural language processing of spoken dialogue, and set directions
for future research.
