A study on invariance of f-divergence and its application to speech recognition

IEEE Transactions on Signal Processing

Volumn 58, Issue 7, 2010, Pages 3884-3890

  Qiao, Yu ^a   Minematsu, Nobuaki ^b  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b UNIVERSITY OF TOKYO   (Japan)

Author keywords

F divergence; Invariance to transformation; Speech recognition; Structural representation

Indexed keywords

ACOUSTIC MODELING; F-DIVERGENCE; FUNDAMENTAL PROBLEM; GAUSSIAN MIXTURES; GAUSSIANS; INVARIANCE OF F-DIVERGENCE; INVARIANT REPRESENTATION; JAPANESE VOWELS; RECOGNITION PERFORMANCE; SEQUENCE DATA; SPEECH SOUNDS; STRUCTURAL REPRESENTATION;

HIDDEN MARKOV MODELS; MAXIMUM LIKELIHOOD; SIGNAL PROCESSING;

SPEECH RECOGNITION;

EID: 77953734207     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2010.2047340     Document Type: Article

References (22)

1. M. Benzeghiba et al., "Automatic speech recognition and speech variability: A review.," Speech Commun., vol.49, no.10-11, pp. 763-786, 2007.
2. M. Pitz and H. Ney, "Vocal tract normalization equals linear transformation in cepstral space," IEEE Trans. Speech Acoust. Process., vol.13, no.5, pp. 930-944, 2005.
3. N. Minematsu, "Yet another acoustic representation of speech sounds," Proc. ICASSP, pp. 585-588, 2004.
4. Y. Qiao, S. Asakawa, and N. Minematsu, "Random discriminant structure analysis for automatic recognition of connected vowels," Proc. ASRU, pp. 576-581, 2007.
5. S. Asakawa, N. Minematsu, and K. Hirose, "Multi-stream parameterization for structural speech recognition," Proc. ICASSP, pp. 4097-4100, 2008.
6. N. Minematsu et al., "Structural representation of the pronunciation and its use for CALL," in Proc. IEEE Spoken Lang. Tech. Workshop, 2006, pp. 126-129.
7. S. Saito, D. Asakawa, N. Minematsu, and K. Hirose, "Structure to speech-speech generation based on infant-like vocal imitation," Proc. INTERSPEECH, pp. 1837-1840, 2008.
8. I. Csiszar, "Information-type measures of difference of probability distributions and indirect," Stud. Sci. Math. Hung., vol.2, pp. 299-318, 1967.
9. S. M. Ali and S. D. Silvey, "A general class of coefficients of divergence of one distribution from another," J. Royal Statist. Soc. Ser. B, vol.28, no.1, pp. 131-142, 1966.
10. F. Liese and I. Vajda, "Convex statistical distances," in Teubner-Texte zur Mathematik. Leipzig, Germany: B.G. Teubner Verlagsgesellschaft, 1987.
11. M. Pardo and I. Vajda, "About distances of discrete distributions satisfying the dataprocessing theorem of information theory," IEEE Trans. Inf. Theory, vol.43, pp. 1288-1293, 1997.
12. Y. Qiao and N. Minematsu, " divergence is a generalized invariant measure between distributions," Proc. INTERSPEECH, pp. 1349-1352, 2008.
13. Y. Qiao, N. Minematsu, and K. Hirose, "On invariant structural representation for speech recognition: Theoretical validation and experimental improvement," Proc. INTERSPEECH, pp. 3055-3058, 2009.
14. N. Minematsu and Y. Qiao, Speech Recognition Using the Invariance of -Divergence Tech. Report, Univ. Tokyo, 2009.
15. I. Csiszar and P. Shields, Information Theory And Statistics: A Tutorial. New York: Now , 2004.
16. I. Vajda, "On the -divergence and singularity of probability measures," Perio. Math. Hungar, vol.2, pp. 223-234, 1972.
17. F. Liese and I.Vajda, "On divergences and informations in statistics and information theory," IEEE Trans. Inf. Theory, vol.52, pp. 4394-4412, 2006.
18. M. Do, "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models," IEEE Signal Process. Lett., vol.10, pp. 115-118, 2003.
19. J. Hershey and P. Olsen, "Approximating the Kullback Leibler divergence between Gaussian mixture models," Proc. ICASSP, vol.4, pp. 317-320, 2007.
20. J. Goldberger and H. Aronowitz, "A distance measure between GMMs based on the unscented transform and its application to speaker recognition," Proc. Eurospeech, pp. 1985-1989, 2005.
21. N. Minematsu, "Mathematical evidence of the acoustic universal structure in speech," Proc. ICASSP, pp. 889-892, 2005.
22. K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1990.