Performance Evaluation for Voice Conversion Systems

  • Todor Ganchev
  • Alexandros Lazaridis
  • Iosif Mporas
  • Nikos Fakotakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5246)

Abstract

In the present work, we introduce a new performance evaluation measure for assessing the capacity of voice conversion systems to modify the speech of one speaker (source) so that it sounds as if it was uttered by another speaker (target). This measure relies on a GMM-UBM-based likelihood estimator that estimates the degree of proximity between an utterance of the converted voice and the predefined models of the source and target voices. The proposed approach allows the formulation of an objective criterion, which is applicable for both evaluation of the virtue of a single system and for direct comparison (benchmarking) among different voice conversion systems. To illustrate the functionality and the practical usefulness of the proposed measure, we contrast it with four well-known objective evaluation criteria.

Keywords

Performance evaluation voice conversion speaker identification 

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References

  1. 1.
    Abe, M., Nakamura, S., Shikano, K., Kuwabara, H.: Voice conversion through vector quantization. In: Proc. ICASSP 1988, USA, pp. 655–658 (1988)Google Scholar
  2. 2.
    Kreiman, J., Papcun, G.: Comparing, discrimination and recognition of unfamiliar voices. Speech Communication 10(3), 265–275 (1991)CrossRefGoogle Scholar
  3. 3.
    Methods for subjective determination of transmission quality, Tech. Rep. ITU-T Recommendation P.800, ITU, Switzerland (1996)Google Scholar
  4. 4.
    Arslan, L.M.: Speaker transformation algorithm using segmental codebooks (STASC). Speech Communication 28(3), 211–226 (1999)CrossRefGoogle Scholar
  5. 5.
    Kain, A.: High resolution voice transformation. Ph.D. dissertation, OGI, Portland, USA (2001)Google Scholar
  6. 6.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digital Signal Processing 10(1-3), 19–41 (2000)CrossRefGoogle Scholar
  7. 7.
    Sündermann, D., Ney, H., Höge, H.: VTLN-based cross-language voice conversion. In: Proc. ASRU 2003, USA, pp. 676–681 (2003)Google Scholar
  8. 8.
    Stylianou, Y., Cappé, O., Moulines, E.: Continuous probabilistic transform for voice conversion. IEEE Trans. Speech and Audio Processing 6(2), 131–142 (1998)CrossRefGoogle Scholar
  9. 9.
    Sündermann, D., Bonafonte, A., Ney, H., Höge, H.: A study on residual prediction techniques for voice conversion. In: Proc. ICASSP 2005, USA, vol. 1, pp. 13–16 (2005)Google Scholar
  10. 10.
    Kominek, J., Black, A.: The CMU ARCTIC speech databases for speech synthesis research. Technical Report CMU-LTI-03-177, Carnegie Mellon University, Pittsburgh, PA (2003)Google Scholar
  11. 11.
    Slaney, M.: Auditory toolbox. Version 2. Technical Report #1998-010, Interval Research Corporation (1998)Google Scholar
  12. 12.
    Rabiner, L.R., Cheng, M.J., Rosenberg, A.E., McGonegal, C.A.: A comparative performance study of several pitch detection algorithms. IEEE Trans. Acoust. Speech & Signal Proc. 24(5), 399–418 (1976)CrossRefGoogle Scholar
  13. 13.
    Garofolo, J.: Getting started with the DARPA-TIMIT CD-ROM: An acoustic phonetic continuous speech database. National Institute of Standards and Technology (NIST), USA (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Todor Ganchev
    • 1
  • Alexandros Lazaridis
    • 1
  • Iosif Mporas
    • 1
  • Nikos Fakotakis
    • 1
  1. 1.Wire Communications Laboratory, Dept. of Electrical and Computer EngineeringUniversity of PatrasRion-PatrasGreece

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