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Speech Processing - Speaker Clustering and Verification

DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA


Non-parametric Bayesian methods have recently gained popularity in several research areas dealing with unsupervised learning. These models are capable of simultaneously learning the cluster models as well as their number based on properties of a dataset. The most commonly applied models are using Dirichlet process priors and Gaussian models, called as Dirichlet process Gaussian mixture models (DPGMMs). Recently, von Mises-Fisher mixture models (VMMs) have also been gaining popularity in modelling high-dimensional unit-normalized features such as text documents and gene expression data.

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Authors:
Ulpu Remes, Okko Räsänen
Submitted On:
28 February 2017 - 7:05am
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i-vector clustering with DPMMs

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[1] Ulpu Remes, Okko Räsänen, "DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1504. Accessed: Dec. 17, 2017.
@article{1504-17,
url = {http://sigport.org/1504},
author = {Ulpu Remes; Okko Räsänen },
publisher = {IEEE SigPort},
title = {DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA},
year = {2017} }
TY - EJOUR
T1 - DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA
AU - Ulpu Remes; Okko Räsänen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1504
ER -
Ulpu Remes, Okko Räsänen. (2017). DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA. IEEE SigPort. http://sigport.org/1504
Ulpu Remes, Okko Räsänen, 2017. DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA. Available at: http://sigport.org/1504.
Ulpu Remes, Okko Räsänen. (2017). "DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA." Web.
1. Ulpu Remes, Okko Räsänen. DIRICHLET PROCESS MIXTURE MODELS FOR CLUSTERING I-VECTOR DATA [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1504