This paper describes a system that exploits the paralinguistic information in the speech to estimate the speakers age and gender. Compared with previously published work, the so called AGENDER approach involves finer grained speaker classes and achieves a significantly higher classification accuracy. The introduction encompasses various application examples representing the actual AGENDER project context. Then hypotheses, method and a representative selection of results from extensive corpus analyses are presented, that build the empirical basis for the machine learning. Finally, the AGENDER approach on speaker classification is outlined, involving the comparison of different classification methods as well as evaluation results. The paper finishes with an outlook on extensions that are scheduled for the next project phase.
Cite as: Müller, C. (2006) Automatic recognition of speakers² age and gender on the basis of empirical studies. Proc. Interspeech 2006, paper 1031-Wed3CaP.11, doi: 10.21437/Interspeech.2006-195
@inproceedings{muller06_interspeech,
author={Christian Müller},
title={{Automatic recognition of speakers² age and gender on the basis of empirical studies}},
year=2006,
booktitle={Proc. Interspeech 2006},
pages={paper 1031-Wed3CaP.11},
doi={10.21437/Interspeech.2006-195}
}