Infinite Models for Speaker Clustering
In this paper we propose the use of infinite models for the clustering of speakers. Speaker segmentation is obtained trough a Dirichlet Process Mixture (DPM) model which can be interpreted as a flexible model with an infinite a priori number of components. Learning is based on a Variational Bayesian approximation of the infinite sequence. DPM model is compared with fixed prior systems learned by ML/BIC, MAP/BIC and a Variational Bayesian method. Experiments are run on a speaker clustering task on the NIST-96 Broadcast News database.
- URL: http://publications.idiap.ch/downloads/papers/2006/valente-Icslp-2006.pdf
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/valente:rr06-19
Record created on 2010-02-11, modified on 2016-08-08