Abstract
Gender classification based on speech signal is an important task in variant fields such as content-based multimedia. In this paper we propose a novel and efficient method for gender classification based on neural network. In our work pitch feature of voice is used for classification between males and females. Our method is based on an MLP neural network. About 96 % of classification accuracy is obtained for 1 second speech segments.
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© 2007 International Federation for Information Processing
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Mostafa Rahimi Azghadi, S., Reza Bonyadi, M., Shahhosseini, H. (2007). Gender Classification Based on FeedForward Backpropagation Neural Network. In: Boukis, C., Pnevmatikakis, A., Polymenakos, L. (eds) Artificial Intelligence and Innovations 2007: from Theory to Applications. AIAI 2007. IFIP The International Federation for Information Processing, vol 247. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74161-1_32
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DOI: https://doi.org/10.1007/978-0-387-74161-1_32
Publisher Name: Springer, Boston, MA
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