Abstract
Speaker Diarization aims at inferring who spoke when in an audio stream and involves two simultaneous unsupervised tasks: (1) the estimation of the number of speakers, and (2) the association of speech segments to each speaker. Most of the recent efforts in the domain have addressed the problem using machine learning techniques or statistical methods (for a review see [11]) ignoring the fact that the data consists of instances of human conversations.
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Valente, F., Vinciarelli, A. (2014). Speaker Diarization of Multi-party Conversations Using Participants Role Information: Political Debates and Professional Meetings. In: Murray-Smith, R. (eds) Mobile Social Signal Processing. MSSP 2010. Lecture Notes in Computer Science, vol 8045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54325-8_3
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DOI: https://doi.org/10.1007/978-3-642-54325-8_3
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