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.

Index Terms: age recognition, Gaussian mixture model (GMM), support vector machine (SVM), weights supervector.

@inproceedings{porat10_interspeech,
  title     = {{Age recognition based on speech signals using weights supervector}},
  author    = {Royi Porat and Dan Lange and Yaniv Zigel},
  year      = {2010},
  booktitle = {{Interspeech 2010}},
  pages     = {2814--2817},
  doi       = {10.21437/Interspeech.2010-744},
  issn      = {2958-1796},
}