Robust front-end and back-end processing for feature extraction for Hindi speech recognition

2010 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2010

Volumn , Issue , 2010, Pages 303-306

  Mehta, Kannu ^a,b   Anand, R S ^b  

^a Intel India Technology Pvt Ltd   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Back end processing; Feature extraction; Gammatone filterbank; Robust Speech Recognition

Indexed keywords

ADDITIVE WHITE GAUSSIAN NOISE; ARMA FILTERING; BACK-END PROCESSING; CEPSTRAL COEFFICIENTS; CLEAN ENVIRONMENT; FEATURE ENHANCEMENT; FEATURE EXTRACTION ALGORITHMS; GAMMATONE FILTERBANK; NOISY ENVIRONMENT; NON-LINEARITY; NORMALIZED FILTERS; PERFORMANCE IMPROVEMENTS; POWER LAW; ROBUST SPEECH RECOGNITION; SPEECH SPECTRA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONTINUOUS SPEECH RECOGNITION; FILTER BANKS; GAUSSIAN NOISE (ELECTRONIC); SIGNAL TO NOISE RATIO; SPEECH PROCESSING; WHITE NOISE;

FEATURE EXTRACTION;

EID: 79951799262     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCIC.2010.5705781     Document Type: Conference Paper

References (13)

1. Koo, J.D. Gibson and SD. Gray (19891, "Filtering of colored noise for speech enhancement and coding", Proc. 288 Y. Gong/Speech Communication 16 (1995) 261-291 IEEE Internat. Conf Acoust. Speech Signal Process., Glasgow, Scotland, May 1989, pp. 349-352.
2. Mokbel and G. Chollet (1991a), "Speech recognition in adverse environments: speech enhancement and spectral transformations", Proc. IEEE Internat. Conf Acoust. Speech Signal Process. 1991, pp. 925-928.
3. Gong, Y.: Speech Recognition in Noisy Environments: A Survey. Speech Communication, Vol. 16, No. 3 (1995) 261-291.
4. X. Huang, A. Acero, and H. Hon, Spoken Language Processing. Upper Saddle River, NJ: Prentice Hall PTR, 2001.
5. B. C. J. Moore, An Introduction to the Psychology of Hearin, San Diego, CA: Academic Press, 2003.
6. Y. Shao and D. L. Wang, "Robust speaker identificati-on using auditory features and computational auditory sc-ene analysis," in Proc. IEEE ICASSP, 2008, pp. 1589-1592.
7. Abdulla, W. H. (2002). Auditory based feature vectors for speech recognition systems, in Advances in Communications and Software Technologies, N.E.Mastorakis & V.V. Kluev, Editor. WSEAS Press. pp 231-236.
8. Aertsen, A., and Johannesma, P. (1980). "Spectro-temporal receptive fields of auditory neurons in the grass frog. I. Characterization of tonal and natural stimuli." Biol. Cybern., 38, 223-234. (Pubitemid 11220294)
9. C. Kim and R. M. Stern. Feature extraction for robust speech recognition using a power-law nonlinearity and power-bias subtraction. In INTERSPEECH-2009, pages 28.31, Sept. 2009.
10. C.-P. Chen, J. Bilmes, and D. Ellis, Blind MVA speech feature processing on Aurora 2.0 Dept. Elect. Eng., Univ. Washington, Seattle, WA, Tech. Rep. UWEETR-2004-0017, 2004.
11. Chen, C. P., Bilmes, J., Ellis, D. P. W.: Speech Feature Smoothing for Robust ASR. Proceedings of ICASSP'05 (2005) 525-528.
12. Cooke, M. (1993). "Modeling Auditory Processing and Organization", Cambridge University Press, U.K.
13. Slaney, M. (1993). "An efficient implementation of the Patterson-Holdsworth auditory filter bank." Apple Computer Technical Report #35, Apple Computer Inc.