Publication Details
Bilinear Factor Analysis for iVector Based Speaker Verification
speaker recognition, i-Vectors, PLDA
In this study, we have proposed and tested a new extension of the PLDA model, where within-class (channel) variability is modeled as a function of the class (speaker) location in the feature (iVector) space.
The combination of iVector extraction and Probabilistic Linear Discriminant Analysis (PLDA) model forms a basis of the current state of the art speaker verification. The PLDA model makes an assumption that the within-speaker (or inter-session) variability in the iVector space is independent of speaker identity. In this work we propose a new model, which can be seen as an extension of PLDA, relaxing this assumption and allowing the within-speaker variability to be different for different locations of speakers in the iVector space. The potential of the proposed model is demonstrated in preliminary experiments.
@inproceedings{BUT111443,
author="Yun {Lei} and Lukáš {Burget} and Nicolas {Scheffer}",
title="Bilinear Factor Analysis for iVector Based Speaker Verification",
booktitle="Proceedings of Interspeech",
year="2012",
pages="1--4",
publisher="International Speech Communication Association",
address="Portland, Oregon",
isbn="978-1-62276-759-5",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2012/lei_interspeech2012_1392_Paper.pdf"
}