Advertisement

Identification of Electronic Disguised Voices in the Noisy Environment

  • Wencheng Cao
  • Hongxia WangEmail author
  • Hong Zhao
  • Qing Qian
  • Sani M. Abdullahi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Since voice disguise has been an increasing tendency in illegal application, which brings great negative impact on establishing the authenticity of audio evidence for audio forensics, especially in noisy environment. Thus it is very important to have the capability of identifying whether a suspected voice has been disguised or not. However, few studies about identification in noisy environment have been reported. In this paper, an algorithm based on linear frequency cepstrum coefficients (LFCC) statistical moments and Formant statistical moments is proposed to identify such condition. First, LFCC statistical moments including mean values and variance unbiased estimation values, and Formant statistical moments including mean values are extracted as acoustic features, and then Support vector machine (SVM) classifiers are used to separate disguised voices from original voices. Experimental results verify the excellent performance of the proposed scheme in the noisy environment.

Keywords

Identification Electronic disguised voices LFCC Formant Noisy environment SVM 

Notes

Acknowledgement

This work was supported by the National Science Foundation of China (NSFC) under the grant No. U1536110, partly supported by the National Natural Science Foundation of China under the grant No. 61402219.

References

  1. 1.
    Perrot, P., Aversano, G., Chollet, G.: Voice disguise and automatic detection: review and perspectives. In: Stylianou, Y., Faundez-Zanuy, M., Esposito, A. (eds.). LNCS, vol. 4391, pp. 101–117Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71505-4_7 CrossRefGoogle Scholar
  2. 2.
    Rodman, R.: Speaker recognition of disguised voices: a program for research. In: Proceedings of the Consortium on Speech Technology in Conjunction with the Conference on Speaker Recognition by Man and Machine: Directions for Forensic Applications, pp. 9–22 (1998)Google Scholar
  3. 3.
    Tan, T.J.: The effect of voice disguise on automatic speaker recognition. In: IEEE International Congress on Image and Signal Processing, pp. 3538–3541 (2010)Google Scholar
  4. 4.
    Wu, H.J., Wang, Y., Huang, J.W.: Blind detection of electronic disguised voice. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, pp. 3013–3017 (2013)Google Scholar
  5. 5.
    Gu, B., Sheng, V.S.: A robust regularization path algorithm for ν-support vector classification. In: IEEE Transactions on Neural Networks & Learning Systems, pp. 1–8 (2016)Google Scholar
  6. 6.
    Gu, B., Sheng, V.S., Tay, K.Y., et al.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wu, H.J., Wang, Y., Huang, J.W.: Identification of electronic disguised voices. IEEE Trans. Inf. Forensics Secur. 9(3), 489–500 (2014)CrossRefGoogle Scholar
  8. 8.
    Jingxu, C., Hongchen, Y., Zhanjiang, S.: The speaker automatic identified system and its forensic application. In: Proceedings of the International Symposium on Computing and Information, vol. 1, pp. 96–100 (2004)Google Scholar
  9. 9.
    Kajarekar, S.S., Ferrer, L., Shriberg, E., et al.: SRI’s 2004 NIST speaker recognition evaluation system. In: IEEE International Congress on Acoustics, Pennsylvania USA, pp. 173–176 (2005)Google Scholar
  10. 10.
    Perrot, P., Chollet, G.: The question of disguised voice. J. Acoust. Soc. Am. 123(5), 3878 (2008)CrossRefGoogle Scholar
  11. 11.
    Trehub, S.E., Cohen, A.J., Thorpe, L.A., Morrongiello, B.A.: Development of the perception of musical relations: semitone and diatonic structure. J. Exp. Psychol. Hum. Percept. Perform. 12(3), 295–301 (1986)CrossRefGoogle Scholar
  12. 12.
    Sun, J.N., De-Sheng, F.U., Yong-Hua, X.U.: Speech detection algorithm based on energy -entropy. Comput. Eng. Des. 26(12), 3429–3431 (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wencheng Cao
    • 1
  • Hongxia Wang
    • 1
    Email author
  • Hong Zhao
    • 2
  • Qing Qian
    • 1
  • Sani M. Abdullahi
    • 1
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina
  2. 2.Department of Electrical and Electronic EngineeringSouth University of Science and Technology of ChinaShenzhenChina

Personalised recommendations