In this work we analyze the average information supplied by a forensic speaker recognition system in an informationtheoretical way. The objective is the transparent reporting of the performance of the system in terms of information, according to the needs of transparency and testability in forensic science. This analysis allows the derivation of a proper measure of goodness for forensic speaker recognition, the empirical cross-entropy (ECE), according to previous work in the literature. We also propose an intuitive representation, namely the ECE-plot, which allows forensic scientists to explain the average information given by the evidence analysis process in a clear and intuitive way. Such representation allows the forensic scientist to assess the evidence evaluation process with independence of the prior information, which is province of the court. Then, fact finders may check the average information given by the evidence analysis with the incorporation of prior information. An experimental example following NIST SRE 2006 protocol is presented in order to highlight the adequacy of the proposed framework in the forensic inferential process. An example of the presentation of the average information supplied by the forensic analysis of the speech evidence in court is also provided, simulating a real case.