Advertisement

Speaker Verification Performance Evaluation Based on Open Source Speech Processing Software and TIMIT Speech Corpus

  • Piotr Kłosowski
  • Adam Dustor
  • Jacek Izydorczyk
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 522)

Abstract

Creating of speaker recognition application requires advanced speech processing techniques realized by specialized speech processing software. It is very possible to improve the speaker recognition research by using speech processing platform based on open source software. The article presents the example of using open source speech processing software to perform speaker verification experiments designed to test various speaker recognition models based on different scenarios. Speaker verification efficiency was evaluated for each scenario using TIMIT speech corpus distributed by Linguistic Data Consortium. The experiment results allowed to compare and select the best scenario to build speaker model for speaker verification application.

Keywords

Speaker recognition Speaker verification Speech processing Open source software Speech corpus 

Notes

Acknowledgements

This work was supported by The National Centre for Research and Development (www.ncbir.gov.pl) under Grant number POIG.01.03.01-24-107/12 (Innovative speaker recognition methodology for communications network safety).

References

  1. 1.
    Dustor, A., Kłosowski, P., Izydorczyk, J.: Influence of feature dimensionality and model complexity on speaker verification performance. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 177–186. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  2. 2.
    Dustor, A., Kłosowski, P., Izydorczyk, J.: Speaker recognition system with good generalization properties. In: Proceedings of International Conference on Multimedia Computing and Systems 2014 p. 73, Marrakech, Morocco, IEEE (2014)Google Scholar
  3. 3.
    Rabiner, L.R., Schafer, R.W.: Introduction to digital speech processing. Found. Trends Sig. Process. 1(1–2), 1–194 (2007)CrossRefGoogle Scholar
  4. 4.
    Kłosowski, P., Dustor, A., Izydorczyk, J., Kotas, J., Ślimok, J.: Speech recognition based on open source speech processing software. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 308–317. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  5. 5.
    Beigi, H.: Fundamentals of speaker recognition. Springer, New York (2011) CrossRefzbMATHGoogle Scholar
  6. 6.
    Togneri, R., Pullella, D.: An overview of speaker identification: accuracy and robustness issues. IEEE Circ. Sys. Mag. 11(2), 23–61 (2011)CrossRefGoogle Scholar
  7. 7.
    Tsontzos, G., Orglmeister, R.: CMU Sphinx4 speech recognizer in a Service-oriented Computing style. In: IEEE International Conference on Service-Oriented Computing and Applications (SOCA), pp. 1–4 (2011)Google Scholar
  8. 8.
    Bilmes, J., Bartels, C.: Graphical model architectures for speech recognition. IEEE Sig Process. Mag. 22(5), 89–100 (2005)CrossRefGoogle Scholar
  9. 9.
    Pellom, B., Hacioglu, K.: Recent improvements in the CU SONIC ASR system for noisy speech: the SPINE task. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Hong Kong (Apr 2003)Google Scholar
  10. 10.
    Young, S., Evermann, G., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book. Cambridge University Engineering Department, Cambridge, UK (2002) Google Scholar
  11. 11.
    Bonastre, J.F., Wils, F., Meignier, S.: ALIZE, a free toolkit for speaker recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’05), vol. 1, pp. 737–740 (2005)Google Scholar
  12. 12.
    Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Front-end feature extraction algorithm; Compression algorithms. Technical standard ES 201 108, v1.1.3. European Telecommunications Standards Institute (2003)Google Scholar
  13. 13.
    Fauve, B.G.B., Matrouf, D., Scheffer, N., Bonastre, J.F., Mason, J.S.D.: State-of-the-art performance in text-independent speaker verification through open-source software. IEEE Trans. Audio, Speech, Lang. Process. 15(7), 1960–1968 (2007)CrossRefGoogle Scholar
  14. 14.
    Garofolo, J.S., Lamel, L.F., Fisher, W.M., Fiscus, J.G., Pallett, D.S., Dahlgren, N.L., Zue, V.: TIMIT Acoustic-Phonetic Continuous Speech Corpus. Linguistic Data Consortium, Philadelphia (1993) Google Scholar
  15. 15.
    Fisher, W.M., Doddington, G.R., Goudie-Marshall, K.M.: The DARPA speech recognition research database: specifications and status. In: Proceedings of DARPA Workshop on Speech Recognition, pp. 93–99 (1986)Google Scholar
  16. 16.
    Fernandez, S., Graves, A., Schmidhuber, J.: Phoneme recognition in TIMIT with BLSTM-CTC (2008)Google Scholar
  17. 17.
    Lopes, C., Perdigao, F.: Phoneme Recognition on the TIMIT Database (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Piotr Kłosowski
    • 1
  • Adam Dustor
    • 1
  • Jacek Izydorczyk
    • 1
  1. 1.Silesian University of TechnologyInstitute of ElectronicsGliwicePoland

Personalised recommendations