GMM-based significance decoding

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 6827-6831

  Abdelaziz, Ahmed Hussen ^a   Zeiler, Steffen ^a   Kolossa, Dorothea ^a   Leutnant, Volker ^b   Haeb Umbach, Reinhold ^b  

^a RUHR UNIVERSITY BOCHUM   (Germany)

^b UNIVERSITY OF PADERBORN   (Germany)

Author keywords

modified imputation; noise robust speech recognition; significance decoding; uncertainty decoding; Uncertainty of observation

Indexed keywords

AUTOMATIC SPEECH RECOGNITION SYSTEM; GAUSSIAN MIXTURE MODEL; MATHEMATICAL FRAMEWORKS; MODIFIED IMPUTATIONS; NOISE ROBUST SPEECH RECOGNITION; REVERBERANT ENVIRONMENT; UNCERTAINTY DECODING; UNCERTAINTY-OF-OBSERVATION;

ACOUSTIC NOISE; MATHEMATICAL TECHNIQUES; SIGNAL PROCESSING; SPEECH RECOGNITION; UNCERTAINTY ANALYSIS;

DECODING;

EID: 84890521674     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638984     Document Type: Conference Paper

References (24)

1. P. Vary and R. Martin, Digital Speech Transmission. John Wiley &Sons, 2006
2. Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms, ETSI, ES.202.050 Std., 2003
3. J. Droppo, L. Deng, and A. Acero, "Evaluation of the SPLICE algorithm on the Aurora2 database," in Proc. Eurospeech, Aalborg, Denmark, September 2001, pp. 217-220
4. P. C. Loizou, Speech enhancement: theory and practice. CRC Taylor and Francis, 2007
5. M. Delcroix, S. Watanabe, and T. Nakatani, Robust Speech Recognition of Uncertain or Missing Data. Springer, 2011, ch. Variance Compensation for Recognition of Reverberant Speech with Dereverberation Preprocessing, pp. 225-255
6. J. A. Arrowood andM. A. Clements, "Using observation uncertainty in HMM decoding," in Proc. International Conference on Spoken Language Processing, Denver, Colorado, Sepember 2002
7. L. Deng, J. Droppo, and A. Acero, "Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion," IEEE Transactions on Speech and Audio Processing, vol. 13, no. 3, pp. 412-421, 2005
8. J. Droppo, A. Acero, and L. Deng, "Uncertainty decoding with SPLICE for noise robust speech recognition," in Proc. The International Conference on Acoustics, Speech, and Signal Processing, vol. I, Orlando, Florida, USA, May 2002, pp. 57-60
9. H. Liao and M. J. F. Gales, "Joint uncertainty decoding for noise robust speech recognition," in Proc. Interspeech, Lisbon, Portugal, Sep. 2005
10. V. Ion and R. Haeb-Umbach, "A novel uncertainty decoding rule with applications to transmission error robust speech recognition," IEEE Trasactions on Audio, Speech and Language Processing, vol. 16, no. 5, pp. 1047-1060, 2008
11. D. Kolossa, A. Klimas, and R. Orglmeister, "Separation and robust recognition of noisy, convolutive speech mixtures using time-frequency masking and missing data techniques," in Proc. IEEE Workshop on Application of Signal Processing to Audio and Acoustics, Mohonk Mountain House, New Paltz, New York, USA, October 2005, pp. 82-85
12. A. H. Abdelaziz, S. Zeiler, and D. Kolossa, "Audio-visual speech recognition for uncertain acoustical observations," in Proc. ITG Fachtagung Sprachkommunikation, Braunschweig, Germany, September 2012
13. A. H. Abdelaziz and D. Kolossa, "Decoding of uncertain features using the posterior distribution of the clean data for robust speech recognition," in Proc. Interspeech, Portland, Oregon, USA, Sptember 2012
14. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, February 1989
15. M. Cooke, J. Barker, S. Cunningham, and X. Shao, "An audiovisual corpus for speech perception and automatic speech recognition," The Journal of the Acoustical Society of America, vol. 120, no. 5, pp. 2421-2424, November 2006
16. J. Bilmes, "A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models," ICSI, Tech. Rep. TR-97-021, 1997
17. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Wiley-Interscience, 2001, ch.Maximum-Likelihood and Bayesian parameter estimation, pp. 95-97
18. H. Liao and M. Gales, "Issues with uncertainty decoding," Speech Communication, vol. 50, no. 4, pp. 265-277, April 2008
19. Institute for Perception-TNO and Speech Research Unit-RSRE, retrieved November 2012[Online]. Available: http://spib.rice.edu/spib/data/signals/noise/
20. "Sound ideas," The General Sound Effects Library, Series 6000, retrieved November 2012[Online]. Available: http://www.sound-ideas.com/6000.html
21. A. Kitzig, "Niederrhein University room impulse response package information," Niederrhein University of Applied Sciences, Tech. Rep., June 2010
22. I. Cohen, "Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging," IEEE Transactions on Speech and Audio, vol. 11, no. 5, p. 466-475, 2003
23. R. F. Astudillo, D. Kolossa, and R. Orglmeister, "Accounting for the uncertainty of speech estimates in the complex domain for minimum mean square error speech enhancement," in Proc. Interspeech, Brighton, United Kingdom, 2009
24. A. Vorwerk, S. Zeiler, D. Kolossa, R. F. Astudillo, and D. Lerch, Robust Speech Recognition of Uncertain or Missing Data. Springer, 2011, ch. Use ofMissing and Unreliable Data for Audiovisual Speech Recognition, pp. 345-373.