A feature extraction method using subband based periodicity and aperiodicity decomposition with noise robust frontend processing for automatic speech recognition

Speech Communication

Volumn 48, Issue 11, 2006, Pages 1447-1457

  Ishizuka, Kentaro ^a   Nakatani, Tomohiro ^a  

^a Japan   (Japan)

Author keywords

Aperiodicity; Noise robust frontend; Periodicity; Speech feature; Subband

Indexed keywords

DATABASE SYSTEMS; FREQUENCY DOMAIN ANALYSIS; PARAMETER ESTIMATION; ROBUSTNESS (CONTROL SYSTEMS); SPEECH ANALYSIS; SPEECH RECOGNITION;

APERIODICITY; NOISE ROBUST FRONTEND; PERIODICITY; SPEECH FEATURE; SPEECH PERCEPTION; SUBBAND;

FEATURE EXTRACTION;

EID: 33750352847     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2006.06.008     Document Type: Article

References (39)

1. Adami A., Burget L., Duponi S., Garudadri H., Grezl F., Hermansky H., Jain P., Kajarekar S., Morgan N., and Sivadas S. QUALCOMM-ICSI-OGI features for ASR. Proc. ICSLP (2002) 21-24
2. Aikawa K., Singer H., Kawahara H., and Tohkura Y. Cepstral representation of speech motivated by time-frequency masking: an application to speech recognition. J. Acoust. Soc. Am. 100 (1996) 603-614
3. Ali A.M., Spiegel J.V., and Mueller P. Robust auditory-based speech processing using the average localized synchrony detection. IEEE Trans. Speech Audio Process. 10 (2002) 279-292
4. Atal B.S. Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J. Acoust. Soc. Am. 55 (1974) 1304-1312
5. Bernard A., Gong Y., and Cui X. Can back-ends be more robust than front-ends? Investigation over the Aurora-2 database. Proc. ICASSP 1 (2004) 1025-1028
6. Berouti M., Schwartz R., and Makhoul J. Enhancement of speech corrupted by acoustical noise. Proc. ICASSP (1979) 208-211
7. Boll S. Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Process. ASSP-27 (1979) 113-120
8. Chen C.P., Filali K., and Bilmes J.A. Frontend post-processing and backend model enhancement on the AURORA 2.0/3.0 databases. Proc. ICSLP (2002) 241-244
9. Davis S.B., and Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Process. ASSP-28 (1980) 357-366
10. de Cheveigné A., McAdams S., and Marin C.M.H. Concurrent vowel identification. II. Effects of phase, harmonicity, and task. J. Acoust. Soc. Am. 101 (1997) 2848-2856
11. ETSI standard document, 2003. Speech processing, transmission and quality aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms. ETSI ES 202 050 v1.1.3.
12. Gales M., and Young S. HMM recognition in noise using parallel model combination. Proc. Eurospeech (1993) 837-840
13. Gao Y., Huang T., Chen S., and Haton J.-P. Auditory model based speech processing. Proc. ICSLP (1992) 73-76
14. Ghitza O. Temporal non-place information in the auditory nerve firing patterns as a front-end for speech recognition in a noisy environment. J. Phonetics 16 (1988) 109-124
15. Gong Y. Speech recognition in noisy environments: a survey. Speech Comm. 16 (1995) 261-291
16. Greenberg S. Speech processing in the auditory system: an overview. In: Greenberg S., Ainsworth W.A., Popper A.N., and Fay R.R. (Eds). Speech Processing in the Auditory System (2004), Springer-Verlag, New York
17. Hermansky H. Perceptual linear predictive (PLP) analysis of speech. J. Acoust. Soc. Am. 87 (1990) 1738-1752
18. Hess W. Pitch Determination of Speech Signals (1983), Springer-Verlag, New York
19. Hirsh, H.G., Pearce, D., 2000. The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In Proc. of the ISCA Tutorial and Research Workshop on Automatic Speech Recognition (ISCA ITRW ASR), pp. 181-188.
20. Ishizuka K., and Miyazaki N. Speech feature extraction method representing periodicity and aperiodicity in sub bands for robust speech recognition. Proc. ICASSP 1 (2004) 141-144
21. Ishizuka K., Miyazaki N., Nakatani T., and Minami Y. Improvement in robustness of speech feature extraction method using sub-band based periodicity and aperiodicity decomposition. Proc. ICSLP 2 (2004) 937-940
22. Itakura F. Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. ASSP-23 (1975) 67-72
23. Jackson P.J.B., Moreno D.M., Russell M.J., and Hernando J. Covariation and weighting of harmonically decomposed streams for ASR. Proc. Eurospeech (2003) 2321-2324
24. Kajita S., and Itakura F. Robust feature extraction using SBCOR analysis. Proc. ICASSP (1995) 421-424
25. Kim D.S., Lee S.Y., and Kil R.M. Auditory processing of speech signals for robust speech recognition in real-world noisy environments. IEEE Trans. Speech Audio Process. 7 (1999) 55-69
26. Li Q., Soong F.K., and Siohan O. An auditory system-based feature for robust speech recognition. Proc. Eurospeech (2001) 619-621
27. Lim J., and Oppenheim A. All-pole modeling of degraded speech. IEEE Trans. Acoust. Speech Signal Process. ASSP-26 (1978) 197-210
28. Lockwood P., and Boudy J. Experiments with a nonlinear spectral subtractor (NSS), hidden Markov models and the projection, for robust speech recognition in cars. Speech Comm. 11 (1992) 215-228
29. Macho D., Mauuary L., Noé B., Cheng Y.M., Ealey D., Jouvet D., Kelleher H., Pearce D., and Saadoun F. Evaluation of a noise-robust DSR front-end on AURORA databases. Proc. ICSLP (2002) 17-20
30. Mauuary L. Blind equalization in the cepstral domain for robust telephone based speech recognition. Proc. EUSIPCO 1 (1998) 359-362
31. Minami Y., and Furui S. A maximum likelihood procedure for a universal adaptation method. Proc. ICASSP (1995) 129-132
32. Moore B.C.J., and Glasberg B.R. Suggested formula for calculating auditory-filter bandwidths and excitation patterns. J. Acoust. Soc. Am. 74 (1983) 750-753
33. Nakamura, S., Yamamoto, K., Takeda, K., Kuroiwa, S., Kitaoka, N., Yamada, T., Mizumachi, M., Nishiura, T., Fujimoto, M., Saso, A., Endo, T., 2003. Data collection and evaluation of AURORA-2 Japanese corpus. In: Proc. of the 8th IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 619-623.
34. Nakamura S., Takeda K., Yamamoto K., Yamada T., Kuroiwa S., Kitaoka N., Nishiura T., Sasou A., Mizumachi M., Miyajima C., Fujimoto M., and Endo T. AURORA-2J: an evaluation framework for Japanese noisy speech recognition. IEICE Trans. Inform. Systems E88-D (2005) 535-544
35. Patterson R.D. Auditory filter shapes derived with noise stimuli. J. Acoust. Soc. Am. 59 (1976) 640-654
36. Patterson R.D., and Moore B.C.J. Auditory filters and excitation patterns as representations of frequency resolution. In: Moore B.C.J. (Ed). Frequency Selectivity in Hearing (1986), Academic Press, London 23-177
37. Pearce D., and Hirsh H.G. The AURORA experimental framework for the performance evaluation of speech recognition systems under noise conditions. Proc. ICSLP 4 (2000) 29-32
38. Rabiner L.R. On the use of autocorrelation analysis for pitch detection. IEEE Trans. Acoust. Speech Signal Process. 25 (1977) 24-33
39. Seneff S. A joint synchrony/mean-rate model of auditory speech processing. J. Phonetics 16 (1988) 55-76