  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.
