Iterative Unsupervised GMM Training for Speaker Indexing

Iterative Unsupervised GMM Training for Speaker Indexing

The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn't require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that th...

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Bibliographic Details
Main Authors: R. Jarina, M. Paralic
Format: Article
Language:English
Published: Spolecnost pro radioelektronicke inzenyrstvi 2007-09-01
Series:Radioengineering
Subjects:
Speaker indexing
training
iteration
log. likelihood
Gaussian Mixture Model (GMM)
Online Access:http://www.radioeng.cz/fulltexts/2007/07_03_138_144.pdf
Description
Summary:The paper addresses a novel algorithm for speaker searching and indexation based on unsupervised GMM training. The proposed method doesn't require a predefined set of generic background models, and the GMM speaker models are trained only from test samples. The constrain of the method is that the number of the speakers has to be known in advance. The results of initial experiments show that the proposed training method enables to create precise GMM speaker models from only a small amount of training data.
ISSN:1210-2512

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