Confidence measures for speaker segmentation and their relation to speaker verification

This paper addresses the problem of speaker verification in two speaker conversations, proposing a set of confidence measures to assess the quality of a given speaker segmentation. In addition we study how these measures can be used to estimate the performance of a state of the art speaker verification system. Our approach for speaker segmentation is based on the eigenvoice paradigm. We present a novel PCA based initialization in the speaker factor space along with a modification of the speaker turn duration distribution that improves the performance of previously reported Joint Factor Analysis based speaker segmentation systems. Three confidence measures are analyzed on the output of the proposed segmentation system for the summed-channel telephone data of the NIST Speaker Recognition Evaluation 2008, showing that they constitute a good measure to estimate not only the segmentation accuracy but the performance of a speaker verification system when it faces two speaker conversations.