Identifying Lithuanian Native Speakers Using Voice Recognition

  • Laurynas DovydaitisEmail author
  • Vytautas Rudžionis
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 303)


In this paper, we analyze speaker identification and present identification test results on Lithuanian native speakers’ database LIEPA. Two approaches for speaker acoustic modeling are examined. We start by extracting MFCC features from audio samples, then we feed this data to create speaker acoustic model with hidden Markov models (1) and with deep neural networks (2). We compare both methods by nalyzing the subset of samples from LIEPA database. This helps to achieve more than 96% identification accuracy on sample dataset.


Speaker identification Deep neural networks Hidden Markov models 


  1. 1.
    Dovydaitis, L., Rasymas, T., Rudzionis, V.: Speaker Authentication System Based on Voice Biometrics and Speech Recognition, Business Information Systems Workshops, BIS International Workshops, Series Print ISSN 1865–1348 (2016)Google Scholar
  2. 2.
    LIEPA Homepage. Accessed 09 May 2017
  3. 3.
    Tiwari, V.: MFCC and its applications in speaker recognition. Int. J. Emerg. Technol. 1(1), 19–22 (2010)Google Scholar
  4. 4.
    HTK Homepage. Accessed 09 May 2017
  5. 5.
    Abdallah, J.S., Osman, M.I., et al.: Text-independent speaker identification using hidden markov model. World Comput. Sci. Inf. Technol. J. (WCSIT) 2(6), 203–208 (2012). ISSN: 2221–0741Google Scholar
  6. 6.
    Fandrianto A., Jin, A., Neelappa, A.: Speaker Recognition Using Deep Belief Networks [CS 229] Fall 2012:12-14-12Google Scholar
  7. 7.
    Garcia-Romero, D., Zhang, X., Alan McCree, A., Povey, D.: Improving speaker recognition performance in the domain adaptation challenge using deep neural networks. In: Spoken Language Technology Workshop (SLT), IEEE (2014)Google Scholar
  8. 8.
    Graves, A., Mohamed, A., et al.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013)Google Scholar
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Keras hompage. Accessed 09 May 2017

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kaunas FacultyVilnius UniversityKaunasLithuania

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