Abstract
During online speaker diarization, a situation may occur where a single speaker is being represented by several different models. Such situation leads to worsened diarization results, because the diarization system considers every change of a model to be a change of speakers. In the article we describe a method for detecting this situation and propose several ways of solving it. Experiments show that the most suitable option is treating multiple GMMs as belonging to a single speaker, i.e. updating all of them with the same data every time one of them is assigned a new segment. In that case, there was a relative improvement in Diarization Error Rate of 30.69% in comparison with the baseline system.
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Kunešová, M., Radová, V. (2015). Ideas for Clustering of Similar Models of a Speaker in an Online Speaker Diarization System. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_26
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DOI: https://doi.org/10.1007/978-3-319-24033-6_26
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