@inproceedings{2edc9194ae0e49aaa79da258ee671715,
title = "Factored covariance modeling for text-independent speaker verification",
abstract = "Gaussian mixture models (GMMs) are commonly used to model the spectral distribution of speech signals for text-independent speaker verification. Mean vectors of the GMM, used in conjunction with support vector machine (SVM), have shown to be effective in characterizing speaker information. In addition to the mean vectors, covariance matrices capture the correlation between spectral features, which also represent some salient information about speaker identity. This paper investigates the use of local correlation between different dimensions of acoustic vector by using factor analysis and linear Gaussian model. Log-Euclidean inner product kernel is used to measure the similarity between two speech utterances in the form of covariance matrices. Experiments carried on NIST 2006 speaker verification tasks shows promising results.",
keywords = "covariance modeling, factor analysis, Gaussian mixture model, log-Euclidean, support vector machine",
author = "Eryu Wang and Lee, \{Kong Aik\} and Bin Ma and Haizhou Li and Wu Guo and Lirong Dai",
year = "2011",
month = may,
doi = "10.1109/ICASSP.2011.5947443",
language = "English",
isbn = "9781457705397",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "4856--4859",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",
note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
}