A Study of Generic Models for Unsupervised On-line Speaker Indexing

On-line speaker indexing sequentially detects the points where a speaker identity changes in a multi-speaker audio stream, and classifies each speaker segment. This paper addresses two challenges: The first relates to monitoring which requires on-line processing. The second relates to the fact that the numberlidentity of the speakers is unknown. The indexing needs to be made in a unsupervised process. To address these issues, we apply a predetermined generic speaker-independent model set, Sample Speaker Model(SSM). This set can be useful for more accurate speaker modeling and clustering without requiring training models on target speaker data. Once a speakerindependent model is selected from the sample models, it is adapted into a speaker-dependent model progressively. Experiments were performed with Speaker Recognition Benchmark NIST Speech(1999). Results showed that our new technique, simulated using Markov Chain Monte Carlo Method, gave 92.47% indexing accuracy on telephone conversation data.