EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification

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.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Speaker Identification EVI en-GB Fuzzy Retrieval Top-1 (%) 67.77 # 1
Speaker Identification EVI fr-FR Fuzzy Retrieval Top-1 (%) 80.83 # 1
Speaker Identification EVI pl-PL Fuzzy Retrieval Top-1 (%) 95.13 # 1

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