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Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks

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Speech and Computer (SPECOM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11658))

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Abstract

In the given article, we present a novel approach in the paralinguistic field of age and gender recognition by speaker voice based on deep neural networks. The training and testing of proposed models were implemented on the German speech corpus aGender. We conducted experiments using different network topologies, including neural networks with fully-connected and convolutional layers. In a joint recognition of speaker age and gender, our system reached the recognition performance measured as unweighted accuracy of 48.41%. In a separate age and gender recognition setup, the obtained performance was 57.53% and 88.80%, respectively. Applied deep neural networks provide the best result of speaker age recognition in comparison to existing traditional classification methods.

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Acknowledgements

This research is supported by the Russian Science Foundation (project No. 18-11-00145).

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Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg, Russia

    Maxim Markitantov & Oxana Verkholyak

Authors
  1. Maxim Markitantov
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  2. Oxana Verkholyak
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Corresponding author

Correspondence to Maxim Markitantov .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Albert Ali Salah

  2. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Markitantov, M., Verkholyak, O. (2019). Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_34

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  • DOI: https://doi.org/10.1007/978-3-030-26061-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26060-6

  • Online ISBN: 978-3-030-26061-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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