@inproceedings{cb484941b53843e591e44fce89b06520,
title = "Parameterization of prosodic feature distributions for SVM modeling in speaker recognition",
abstract = "Multiple recent studies have shown that speaker recognition performance using frame-based cepstral features is improved by adding higher-level information, including prosodie and lexical features. This paper explores the important question of finding a good kernel for a system that models syllable-based prosodie features using support vector machines (SVMs). The system has been the best performing of our high-level systems in the last two NIST evaluations, and gives significant improvements when combined with cepstral-based systems. We introduce two new methods for transforming the syllable-level features into a single high-dimensional vector that can be well modeled by SVMs, resulting in significant gains in speaker recognition performance.",
keywords = "GMM, Prosody, SVM, Speaker recognition",
author = "Luciana Ferrer and Elizabeth Shriberg and Sachin Kajarekar and Kemal S{\"o}nmez",
year = "2007",
doi = "10.1109/ICASSP.2007.366892",
language = "English (US)",
isbn = "1424407281",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "IV233--IV236",
booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",
note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",
}