Advertisement

Speaker Recognition in Orthogonal Complement of Time Session Variability Subspace

  • Satoru TsugeEmail author
  • Shingo Kuroiwa
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)

Abstract

A time session variability between the enrollment data and the recognized data degrades speaker recognition performance. Hence, the time session variability is one of the most important issues in the speaker recognition technology. In this paper, we propose a robust speaker recognition method for the time session variability. The proposed method estimates a time session variability subspace. Then, the proposed method carries out the speaker recognition in the orthogonal complement of the time session variability subspace. In addition, we incorporate a linear discriminant analysis method into the proposed method. In order to evaluate the proposed method, we conducted a speaker identification experiment. Experimental results show that the proposed method improves speaker identification performance of baseline.

Keywords

Speaker recognition i-vector Time session variability Deflation Orthogonal complement 

Notes

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP16K00229.

References

  1. 1.
    Kinnunen, T., Li, H.: An overview of text-independent speaker recognition: from features to supervectors. Speech Commun. 52(1), 12–40 (2010)CrossRefGoogle Scholar
  2. 2.
    Matsui, T., Nishitani, T., Furui, S.: A study of model and a priori threshold updating in speaker verification. IEICE Trans. J81-DII(2), 268–276 (1998). (in Japanese)Google Scholar
  3. 3.
    Kenny, P., Boulianne, G., Ouellet, P., Dumouchel, P.: Joint factor analysis versus eigenchannels in speaker recognition. IEEE Trans. Audio Speech Lang. Process. 15(4), 1435–1447 (2007)CrossRefGoogle Scholar
  4. 4.
    Kenny, P., Boulianne, G., Ouellet, P., Dumouchel, P.: Speaker and session variability in GMM-based speaker verification. IEEE Trans. Audio Speech Lang. Process. 15(4), 1448–1460 (2007)CrossRefGoogle Scholar
  5. 5.
    Kenny, P., Ouellet, P., Dehak, N., Gupta, V., Dumouchel, P.: A study of interspeaker variability in speaker verification. IEEE Trans. Audio Speech Lang. Process. 16(5), 980–988 (2008)CrossRefGoogle Scholar
  6. 6.
    Kenny, P.: Bayesian speaker verification with heavy-tailed priors. In: Proceedings of Odyssey (2010)Google Scholar
  7. 7.
    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
  8. 8.
    Makinae, H., Osanai, T., Kamada, T., Tanimoto, M.: Construction and preliminary analysis of a large-scale bone-conducted speech database. IEICE Techn. Rep. Speech 107(165), 97–102 (2007). (in Japanese)Google Scholar
  9. 9.
    Furui, S., Maekawa, K., Isahara, H.: A Japanese national project on spontaneous speech corpus and processing technology. In: Proceedings of ASR 2000, pp. 244–248 (2000)Google Scholar
  10. 10.
    Partridge, M., Calvo, R.A.: Fast dimensionality reduction and simple PCA. Intell. Data Anal. 2, 203–214 (1998)CrossRefGoogle Scholar
  11. 11.
    Tsuge, S., Kuroiwa, S.: AWA long-term recording speech corpus (AWA-LTR). In: Proceedings of 2013 International Workshop on Nonlinear Circuits, Communication and Signal Processing (NCSP 2013), pp. 17–20 (2013)Google Scholar
  12. 12.
    Garcia-Romero, D., Espy-Wilson, C.Y.: Analysis of i-vector length normalization in speaker recognition systems. In: Proceedings of Interspeech, pp. 249–252 (2011)Google Scholar
  13. 13.
    Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J., Stemmer, G., Vesely, K.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding (2011)Google Scholar
  14. 14.
    scikit-learn, machine learning in Python. http://scikit-learn.org/stable/

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Daido UniversityNagoyaJapan
  2. 2.Chiba UniversityChibaJapan

Personalised recommendations