Unsupervised text independent speaker classification

Speaker recognition and verification has been used in a variety of commercial, forensic and military applications. The classical problem is that of supervised recognition, in which there is sufficient a priori information on the speakers to be identified. In such cases, the recognition system has speaker models, estimated during training sessions. This paper deals with the problem of unsupervised speaker classification, where no a priori speaker information is available. The algorithm accepts multi-speaker dialogue speech data, estimates the number of speakers and assigns each speech segment to its speaker. Preliminary results are described.