Forensic Automatic Speaker Classification in the "Coming Paradigm Shift"

• Authors: Joaquin Gonzalez-Rodriguez;Daniel Ramos
• Venue: Speaker Classification I
• Year: 2007

A new paradigm for forensic science has been encouraged in the last years, motivated by the recently reopened debate about the infallibility of some classical forensic disciplines and the controversy about the admissibility of evidence in courts. Standardization of procedures, proficiency testing, transparency in the scientific evaluation of the evidence and testability of the system and protocols are emphasized in order to guarantee the scientific objectivity of the procedures. In this chapter those ideas and their relationship to automatic forensic speaker classification will be analyzed in order to define where automatic speaker classification is and which direction should it take under this context. Following the DNA methodology, which is being regarded as the scientific "golden" standard for evidence evaluation, the Bayesian approach has been proposed as a scientific and logical methodology. Likelihood ratios (LR) are computed based on the similarity-typicality pair, which facilitates the transparency in the process. The speaker classification is performed by the fact finder, who defines the possible hypotheses involved in the classification process. Thus, the prior probability of the hypotheses and the LRcomputed by the forensic system are used to assign a class to each suspected speaker depending on the defined hypotheses. The definition of this hypotheses typically refer to the speaker identity, thus leading to a speaker recognition task, but they can be defined in a more general context of speaker classification. The concept of calibration as a way of reporting reliable and accurate opinions is also addressed. Application-independent evaluation techniques (Cllrand APE curves) are addressed as a proper way for presenting results of proficiency testing in courts, as these evaluation metrics clearly show the influence of calibration errors in the accuracy of the inferential decision process. In order to illustrate the effects of calibration, we conclude with new experimental examples used as blind proficiency test following the NIST SRE 2006 evaluation protocol.