Infinite Models for Speaker Clustering
| Type of publication: | Conference paper |
| Citation: | valente:Icslp:2006 |
| Booktitle: | International Conference on Spoken Language Processing |
| Year: | 2006 |
| Note: | IDIAP-RR 06-19 |
| Crossref: | valente:rr06-19: |
| Abstract: | 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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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