Criminal investigations  rely on the collection of conversational data. The identity of speakers must be assessed in order to build or improve the accuracy of an existing criminal network. Investigators use social network analysis tools  to identify the most central character and the different communities within the network. We introduce  Crime Scene Investigation (CSI) television show as a potential candidate for criminal conversational data. We also introduce the metric of conversation accuracy in the context of criminal investigations. In this paper, a speaker identification baseline is improved by re-ranking candidate speakers based on the frequency of previous interactions between speakers and the topology of the criminal network. The proposed method can be applied to conversations involving two or more speakers. We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Criminal investigations mostly rely on the collection of speech conversational data in order to identify speakers and build or enrich an existing criminal network. Social network analysis tools are then applied to identify the most central characters and the different communities within the network. We introduce two candidate datasets for criminal conversational data, Crime Scene Investigation (CSI) , a television show, and the ROXANNE simulated data. We also introduce the metric of conversation accuracy in the context of criminal investigations. By re-ranking candidate speakers based on the frequency of previous interactions , we improve the speaker identification baseline by 1.2 \% absolute ( 1.3 \% relative), and the conversation accuracy by 2.6 \% absolute ( 3.4 \% relative) on CSI data, and by 1.1\% absolute (1.2\% relative), and 2\% absolute (2.5\% relative) respectively on the ROXANNE simulated data.
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mostly
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conversational data. The identity of speakers must be assessed
speech conversational data
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build or improve the accuracy of
identify speakers and build or enrich
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Investigators use social
Social
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are then applied
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character
characters
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two candidate datasets for criminal conversational data,
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television show as a potential candidate for criminal conversational
, a television show, and the ROXANNE simulated
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In this paper, a speaker identification baseline is improved by
By
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between speakers and the topology of the criminal network. The proposed method can be applied to conversations involving two or more speakers. We show that our approach outperforms the baseline speaker accuracy by 1.3
, we improve the speaker identification baseline by 1.2
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1.5
1.3
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3.7
2.6
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4.7
3.4
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data, and by 1.1\% absolute (1.2\% relative), and 2\% absolute (2.5\% relative) respectively on the ROXANNE simulated