%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{valente:Icslp:2006,
                      author = {Valente, Fabio},
                    projects = {Idiap},
                       title = {Infinite Models for Speaker Clustering},
                   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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2006/valente-Icslp-2006.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/papers/2006/valente-Icslp-2006.ps.gz},
ipdmembership={speech},
}



crossreferenced publications: 
@TECHREPORT{valente:rr06-19,
                      author = {Valente, Fabio},
                    projects = {Idiap},
                       title = {Infinite Models for Speaker Clustering},
                        type = {Idiap-RR},
                      number = {Idiap-RR-19-2006},
                        year = {2006},
                 institution = {IDIAP},
                        note = {Published in ICLSP 2006},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2006/valente-idiap-rr-06-19.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2006/valente-idiap-rr-06-19.ps.gz},
ipdmembership={speech},
}