Who said what? Speaker Identification from Anonymous Minutes of Meetings

Daniel Holmer, Lars Ahrenberg, Julius Monsen, Arne Jönsson, Mikael Apel, Marianna Blix Grimaldi

Keywords: Speaker identification, language features, topic modeling, sentiment analysis, supervised models, BERT-classification

TL;DR: Speaker identification on contributions in minutes from the Swedish Riksbank.

Abstract: We study the performance of machine learning techniques to the problem of identifying speakers at meetings from anonymous minutes issued afterwards. The data comes from board meetings of Sveriges Riksbank (Sweden's Central Bank). The data is split in two ways, one where each reported contribution to the discussion is treated as a data point, and another where all contributions from a single speaker have been aggregated. Using interpretable models we find that lexical features and topic models generated from speeches held by the board members outside of board meetings are good predictors of speaker identity. Combining topic models with other features gives prediction accuracies close to 80% on aggregated data, though there is still a sizeable gap in performance compared to a not easily interpreted BERT-based transformer model that we offer as a benchmark.

Student Paper: Yes, the first author is a student