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        <title>Who said what? Speaker Identification from Anonymous Minutes of Meetings</title>
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    <name type="personal">
        <namePart type="given">Daniel</namePart>
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    <name type="personal">
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            <namePart type="given">Mark</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Who said what? Speaker Identification from Anonymous Minutes of Meetings
%A Holmer, Daniel
%A Ahrenberg, Lars
%A Monsen, Julius
%A Jönsson, Arne
%A Apel, Mikael
%A Grimaldi, Marianna
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F holmer-etal-2023-said
%X 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.
%U https://aclanthology.org/2023.nodalida-1.14
%P 124-134

Markdown (Informal)

[Who said what? Speaker Identification from Anonymous Minutes of Meetings](https://aclanthology.org/2023.nodalida-1.14) (Holmer et al., NoDaLiDa 2023)

• Who said what? Speaker Identification from Anonymous Minutes of Meetings (Holmer et al., NoDaLiDa 2023)

ACL

• Daniel Holmer, Lars Ahrenberg, Julius Monsen, Arne Jönsson, Mikael Apel, and Marianna Grimaldi. 2023. Who said what? Speaker Identification from Anonymous Minutes of Meetings. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 124–134, Tórshavn, Faroe Islands. University of Tartu Library.