Publications

The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker Recognition Evaluation of Human Assisted Speaker Recognition (HASR). We describe our qualitative and automatic speaker comparison processes and our fusion of these processes, which are adapted from USSS casework. The USSS-MIT/LL 2010 HASR results are presented. We also present post-evaluation results. The results are encouraging within the resolving power of the evaluation, which was limited to enable reasonable levels of human effort. Future ideas and efforts are discussed, including new features and capitalizing on naive listeners.

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."