@inproceedings{006974f287594fcbb6cf1c86da6a6ab8,
title = "Age recognition based on speech signals using weights supervector",
abstract = "This paper proposes a new age-recognition system approach - building a Gaussian mixture model-based weights supervector features for a support vector machine (SVM). This approach uses the hypothesis that it is possible to find unique Gaussians for each age-group model in the universal background model (UBM). The weights of those Gaussians can lead to a discriminant way to separate the age groups. The suggested approach was tested on two corpora (aGender and local corpus) with classification into four age groups, achieving 53.75% and 56.18% weighted average recall, respectively, which are better results compared to the state-of-the-art classifier.",
keywords = "Age recognition, Gaussian mixture model (GMM), Support vector machine (SVM), Weights supervector",
author = "Royi Porat and Dan Lange and Yaniv Zigel",
note = "Funding Information: This work was supported in part by the Israel Ministry of Industry and Trade, grant no. 40183, in collaboration with Pudding media.",
year = "2010",
month = jan,
day = "1",
doi = "10.21437/interspeech.2010-744",
language = "English",
series = "Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010",
publisher = "International Speech Communication Association",
pages = "2814--2817",
booktitle = "Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010",
}