A pattern recognition model of voice-based personal verification systems for forensic applications

Abstract

The success of automatic speaker recognition in laboratory environments suggests applications in forensic science for establishing the identity of individuals on the basis of features extracted from speech. A theoretical model for such a verification scheme for continuous normally distributed features is developed. The three cases of using a) single feature, b) multiple independent measurements of a single feature, and c) multiple independent features are explored. The number of independent features needed for a reliable personal identification is computed based on the theoretical model and an exploratory study of some speech features.