Understanding Scores in Forensic Speaker Recognition

Recent work in forensic speaker recognition has introduced many new scoring methodologies. First, confidence scores (posterior probabilities) have become a useful method of presenting results to an analyst. The introduction of an objective measure of confidence score quality, the normalized cross entropy, has resulted in a systematic manner of evaluating and designing these systems. A second scoring methodology that has become popular is support vector machines (SVMs) for high-level features. SVMs are accurate and produce excellent results across a wide variety of token types-words, phones, and prosodic features. In both cases, an analyst may be at a loss to explain the significance and meaning of the score produced by these methods. We tackle the problem of interpretation by exploring concepts from the statistical and pattern classification literature. In both cases, our preliminary results show interesting aspects of scores not obvious from viewing them "only as numbers"