Speaker Detection Without Models

In order to capture sequential information and to take advantage of extended training data conditions, we developed an algorithm for speaker detection that scores a test segment by comparing it directly to similar instances of that speech in the training data. This non-parametric technique, though at an early stage in its development, achieves error rates close to 1% on the NIST 2001 Extended Data task and performs extremely well in combination with a standard Gaussian Mixture Model system. We also present a new scoring method that significantly improves performance by capturing only positive evidence.