Extraction of Audio Features Specific to Speech Production for Multimodal Speaker Detection

IEEE Transactions on Multimedia

Volumn 10, Issue 1, 2008, Pages 63-73

  Besson, Patricia ^a   Popovici, Vlad ^a   Vesin, Jean Marc ^a   Thiran, Jean Philippe ^a   Kunt, Murat ^a  

^a EPFL   (Switzerland)

Author keywords

Audio features differential; evolution multi modal; mutual information; speaker detection; speech

Indexed keywords

EID: 85008046156     PISSN: 15209210     EISSN: 19410077     Source Type: Journal    
DOI: 10.1109/TMM.2007.911302     Document Type: Article

References (36)

1. T. Butz and J.-P. Thiran, “From error probability to information the-oretic (multi-modal) signal processing,” Signal Process., vol. 85, pp. 875–902, 2005.
2. J. W. Fisher and T. Darrell, “Speaker association with signal-level au-diovisual fusion,” IEEE Trans. Multimedia, vol. 6, no. 3, pp. 406–413, Jun. 2004.
3. H. J. Nock, G. 1yengar, and C. Neti, “Speaker localisation using audio-visual synchrony: An empirical study,” in Proc. Int. Conf. Image and Video Retrieval (CIVR), Urbana, IL, Jul. 2003, pp. 488–499.
4. J. Hershey and J. Movellan, “Audio-vision: Using audio-visual syn-chrony to locate sounds,” in Proc. NIPS, Denver, CO, 1999, vol. 12, pp. 813–819.
5. G. Monaci, O. D. Escoda, and P. Vandergheynst, “Analysis of mul-timodal signals using redundant representations,” in Proc. IEEE Int. Conf. Image Processing (ICIP’05), Geneva, Italy, Sep. 2005, pp. 814–820.
6. M. Slaney and M. Covell, “FaceSync: A linear operator for measuring synchronisation of video facial images and audio tracks,” in Proc. NIPS, 2001, vol. 13.
7. P. Smaragdis and M. Casey, “Audio/visual independent components,” in Proc. ICA, Nara, Japan, Apr. 2003, pp. 709–714.
8. T. Butz and J.-P. Thiran, “Feature space mutual information in speech-video sequences,” in Proc. ICME, Lausanne, Switzerland, 2002, vol. 2, pp. 361–364.
9. E. Parzen, “On estimation of a probability density function and mode,” Ann. Mathemat. Statist., vol. 33, pp. 1065–1076, 1962.
10. J. W. Fisher and J. C. Principe, “A methodology for information the-oretic feature extraction,” in Proc. Int. Joint Conf. Neural Networks, Anchorage, AK, May 1998, vol. 3, pp. 1712–1716, ser. IEEE World Congress on Computational Intelligence.
11. R. Storn and K. Price, “Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces,” J. Global Optimiz., vol. 11, pp. 341–359, 1997.
12. T. M. Cover and J. A. Thomas,, D. L. Schilling, Ed., Elements of In-formation Theory. New York: Wiley, 1991.
13. B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artif. Intell., vol. 17, pp. 185–203, 1981.
14. J. Meynet, V. Popovici, and J.-P. Thiran, Face Detection with Mixtures of Boosted Discriminant Features, EPFL, 1015 Ecublens, Tech. Rep. 2005–35, Nov. 2005.
15. P. Besson and M. Kunt, Information theoretic optimization of audio features for multimodal speaker detection, Ecole Polytechnique Fed-erale de Lausanne (EPFL), Lausanne, Switzerland, EPFL-ITS Tech. Rep. 08/2005, Feb. 2005.
16. B. Gold and N. Morgan, Speech and Audio Signal Processing. New York: Wiley, 2000.
17. J. W. Picone, “Signal modeling techniques in speech recognition,” in Proceedings of the IEEE, Sept. 1993, vol. 81, no. 9.
18. A. W. Bowman and A. Azzalini, Applied Smoothing Techniques for Data Analysis. New York: Oxford University Press, 1997.
19. S. Kirkpatrick, C. D. Gelatt, and J. M. P. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 671–680, May 1983.
20. F. Glover, “Future paths for integer programming and links to artificial intelligence,” Comput. and Oper. Res., vol. 13, no. 5, pp. 533–549, 1986.
21. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor: Univ. of Michigan Press, 1975.
22. T. Spalek, P. Pietrzyk, and Z. Sojka, “Application of the genetic algo-rithm joint with the Powell method to nonlinear least-squares fitting of powder EPR spectra,” J. Chem. Inf. Model., vol. 45, pp. 18–29, 2005.
23. A. A. Cole-Rhodes, K. L. Johnson, J. LeMoigne, and I. Zavorin, “Mul-tiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient,” IEEE Trans. Image Process., vol. 12, no. 12, pp. 1495–1511, Dec. 2003.
24. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, 2nd ed. Cambridge, U.K.: Cambridge Uni-versity Press, 1992.
25. P. Besson, M. Kunt, T. Butz, and J.-P. Thiran, “A multimodal approach to extract optimized audio features for speaker detection,” in Proc. Eu-ropean Signal Processing Conf. (EUSIPCO), Antalya, Turkey, Sep. 2005.
26. P. Schroeter, J.-M. Vesin, T. Langenberger, and R. Meuli, “Robust pa-rameter estimation of intensity distributions for brain magnetic reso-nance images,” IEEE Trans. Med. Imag., vol. 17, no. 2, pp. 172–186, Apr. 1998.
27. V. Vaerman, “Multi-dimensional object modeling with application to medical image coding,” Ph.D. dissertation, Ecole Polytechnique Fed-erate de Lausanne (EPFL), Lausanne, Switzerland, 1999.
28. X. Qi, and F. Palmieri, “Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space, Part I: Basic prop-erties of selection and mutation. Part II: Analysis of the diversification role of the crossover,” IEEE Trans. Neural Netw., vol. 5, no. 1, pp. 102–129, Jan. 1994.
29. I. Rechenberg, Evolutionsstrategie Optimierung technischer Systeme nach Prinzipien der biologischen Evolution 1973, Stuttgart: From-mann-Holzboog.
30. H.-P. Schwefel, Numerical Optimization of Computer Models. Chichester, U.K.: Wiley, 1981.
31. K. V. Price, New Ideas in Optimization. New York: McGraw-Hill, 1999, ch. 6, pp. 79–108, An Introduction to Differential Evolution.
32. Y.-W. Leung and Y. Wang, “An orthogonal genetic algorithm with quantization for global numerical optimization,” IEEE Trans. Evol. Comput., vol. 5, no. 1, pp. 41–53. Feb. 2001.
33. R. Joshi and A. C. Sanderson, “Minimal representation multisensor fusion using differential evolution,” IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, vol. 29, no. 1, pp. 63–76, 1999.
34. E. Patterson, S. Gurbuz, Z. Tufekci, and J. Gowdy, “CUAVE: A new audio-visual database for multimodal human-computer interface re-search,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Pro-cessing (ICASSP), Orlando, FL, May 2002, vol. 2, pp. 2017–2020.
35. P. Besson, G. Monaci, P. Vandergheynst, and M. Kunt, Experimental evalutation framework for speaker detection on the CUAVE data-base, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland, Tech. Rep. TR-ITS- 2006.003, Jan. 2006.
36. P. Besson and M. Kunt, “Hypothesis testing as a performance evalua-tion method for multimodal speaker detection,” in Proc. 2nd Int. Work-shop on Biosignal Processing and Classification (BPC2006), ICINCO, Setubal, Portugal, 2006, pp. 106–115.