DANTE Speaker Recognition Module. An Efficient and Robust Automatic Speaker Searching Solution for Terrorism-Related Scenarios

  • Jesús JorrínEmail author
  • Luis BueraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)


The vast amount of data crossing the net with terrorism-related content, including voice, is so immense that the use of powerful filtering/detection tools with great discriminative capacities becomes essential. Although the analysis of this content often ends with some manual inspection, a first filtering process becomes basic. In this direction, we propose a speaker clustering solution based on a speaker identification system. We show that both the speaker clustering and the speaker recognition solution can be used individually to efficiently solve searching tasks in several terrorism-related scenarios.


Automatic speaker recognition Speaker identification Speaker verification Automatic speaker clustering 



The work presented in this paper was supported by the European Commission under contract H2020-700367 DANTE [1].


  1. 1.
    DANTE project homepage. Accessed July 2018
  2. 2.
    Atal, B.S.: Automatic recognition of speakers from their voices. Proc. IEEE 64, 460–475 (1976)CrossRefGoogle Scholar
  3. 3.
    Doddington, G.R.: Speaker recognition-identifying people by their voices. Proc. IEEE 73, 1651–1664 (1985)CrossRefGoogle Scholar
  4. 4.
    Tryon, R.: Clustering Analysis (1993)Google Scholar
  5. 5.
    Dehak, N., Kenny, P., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19(4), 788–798 (2011)CrossRefGoogle Scholar
  6. 6.
    Castaldo, F., Colibro, D., Dalmasso, E., Laface, P., Vair, C.: Compensation of nuisance factors for speaker and language recognition. IEEE Trans. Audio Speech Lang. Process. 15(7), 1969–1978 (2007)CrossRefGoogle Scholar
  7. 7.
    Cumani, S., Laface, P.: Training pairwise support vector machines with large scale datasets. In: 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2014, Florence (Italy), pp. 1664–1668 (2014)Google Scholar
  8. 8.
    Cumani, S., Laface, P.: Large scale training of pairwise support vector machines for speaker recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(11), 1590–1600 (2014)CrossRefGoogle Scholar
  9. 9.
    Lei, Y., Scheffer, N., Ferrer, L., McLaren, M.: A novel scheme for speaker recognition using a phonetically-aware deep neural network. In: Proceedings of ICASSP 2014, pp. 1714–1718 (2014)Google Scholar
  10. 10.
    Cumani, S., Batzu, P.D., Colibro, D., Vair, C., Laface, P., Vasilakakis, V.: Comparison of speaker recognition approaches for real applications. In: Interspeech 2011, Florence, Italy, pp. 2365–2368 (2011)Google Scholar
  11. 11.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Cambridge (2008)zbMATHGoogle Scholar
  12. 12.
    Leeuwen, D.A.V.: Speaker linking in large datasets. In: Odyssey 2010, the Speaker Language and Recognition Workshop, Brno, Czech Republic, pp. 202–208 (2010)Google Scholar
  13. 13.
    Jorrín-Prieto, J.., Vaquero, C., García, P.: Analysis of the impact of the audio database characteristics in the accuracy of a speaker clustering system. In: Odyssey 2016, the Speaker Language and Recognition Workshop, Bilbao, Spain, pp. 393–399 (2016)Google Scholar
  14. 14.
    Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: The relation between the ROC curve and the CMC. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2005), pp. 15–20 (2005)Google Scholar
  15. 15.
    Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1, 1st edn. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nuance Communications, Inc.MadridSpain

Personalised recommendations