Age recognition based on speech signals using weights supervector

Royi Porat, Dan Lange, Yaniv Zigel

Research output: Contribution to conferencePaperpeer-review


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.

Original languageEnglish GB
Number of pages4
StatePublished - 1 Jan 2010


  • Age recognition
  • Gaussian mixture model (GMM)
  • Support vector machine (SVM)
  • Weights supervector

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing


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