11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Age Recognition Based on Speech Signals Using Weights Supervector

Royi Porat, Dan Lange, Yaniv Zigel

Ben-Gurion University of the Negev, Israel

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.

Full Paper

Bibliographic reference.  Porat, Royi / Lange, Dan / Zigel, Yaniv (2010): "Age recognition based on speech signals using weights supervector", In INTERSPEECH-2010, 2814-2817.