Attendance-guided speaker enrollment across meetings

Abstract

Speaker-aware applications rely on enrolled voice profiles, yet collecting enrollment speech from every participant is impractical. Meeting archives provide an alternative source of enrollment speech, comprising recordings of recurring participants paired with expected attendance lists. We investigate whether expected attendance lists can be cross-referenced with diarized anonymous speaker clusters to identify reliable enrollment speech for recurring participants. The resulting task, multi-participant automatic enrollment, must accommodate two types of attendance noise: a listed participant may not speak, and a speaker may be unlisted. To this end, we introduce a benchmark for the task: a synthetic corpus calibrated to an in-house meeting corpus, paired with a contamination-sensitive evaluation protocol. The reference method we introduce scores cross-session cluster chains against expected-attendance patterns and abstains when the evidence is insufficient. We quantify the reference method's benchmark performance as a trade-off between enrollment success and contamination. Applying synthetic-development thresholds unchanged to real meetings exposes a synthetic-to-real gap that remains an open challenge. We will release the benchmark generator and evaluation code.