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Natural Language Processing: Speaker, Language, and Gender Identification with LSTM

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 883))

Abstract

Long short-term memory (LSTM) is a state-of-the-art network used for different tasks related to natural language processing (NLP), pattern recognition, and classification. It has been successfully used for speech recognition and speaker identification as well. The amount of training data and the ratio of training to test data are still the key factors for achieving good results, but have their implications on the real usage. The main contribution of this paper is to achieve a high rate of speaker recognition for text-independent continuous speech using small ratio of training to test data, by applying long short-term memory recursive neural network. A comparison with the probabilistic feed-forward neural network has been made for speaker recognition as well as gender and language identification.

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Acknowledgements

This work was supported by grant S/WI/3/2018 from Bialystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany

    Mohammad K. Nammous

  2. Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Warsaw, Poland

    Mohammad K. Nammous

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

Authors
  1. Mohammad K. Nammous
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  2. Khalid Saeed
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Corresponding author

Correspondence to Mohammad K. Nammous .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari, Mestre, Venice, Venezia, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Nammous, M.K., Saeed, K. (2019). Natural Language Processing: Speaker, Language, and Gender Identification with LSTM. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. https://doi.org/10.1007/978-981-13-3702-4_9

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