Speaker recognition with the Switchboard corpus

We present our development work carried out in preparation for the March´96 speaker recognition test on the Switchboard corpus organized by NIST. The speaker verification system evaluated was a Gaussian mixture model (GMM). We provide experimental results on the development test and evaluation test data, and some experiments carried out since the evaluation comparing the GMM with a phone-based approach. Better performance is obtained by training on data from multiple sessions, and with different handsets. High error rates are obtained even using a phone-based approach both with and without the use of orthographic transcriptions of the training data. We also describe a human perceptual test carried out on a subset of the development data, which demonstrates the difficulty human listeners had with this task