Sub-banded reconstructed phase spaces for speech recognition

Speech Communication

Volumn 48, Issue 7, 2006, Pages 760-774

  Indrebo, Kevin M ^a   Povinelli, Richard J ^a   Johnson, Michael T ^a  

^a Marquette University   (United States)

Author keywords

Dynamical systems; Nonlinear signal processing; Speech recognition; Sub bands

Indexed keywords

ACOUSTIC NOISE; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; SIGNAL PROCESSING; STATISTICAL METHODS;

DYNAMICAL SYSTEMS; NONLINEAR SIGNAL PROCESSING; SUB-BANDS;

SPEECH RECOGNITION;

EID: 33745004604     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2004.12.002     Document Type: Article

References (41)

1. Abarbanel H.D.I. Analysis of observed chaotic data (1996), Springer, New York
2. Allen J.B. How do humans process and recognize speech?. IEEE Trans. Speech Audio Process. 2 (1994) 567-577
3. Badii R., Broggi G., Derighetti B., and Ravini M. Dimension increase in filtered chaotic signals. Phys. Rev. Lett. 60 (1988) 979-982
4. Banbrook M., and McLaughlin S. Is speech chaotic?. IEE Colloquium Exploiting Chaos Signal Process. (1994) 8/1-8/8
5. Banbrook M., McLaughlin S., and Mann I. Speech characterization and synthesis by nonlinear methods. IEEE Trans. Speech Audio Process. 7 (1999) 1-17
6. Bourlard, H., Dupont, S., 1996. A new ASR approach based on independent processing and recombination of partial frequency bands. Internat. Conf. on Spoken Language and Processing (ICSLP), pp. 426-429.
7. Bourlard, H., Dupont, S., 1997. Subband-based speech recognition. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 21-24.
8. Chennaoui A., Pawelzik K., Liebert W., Schuster H.G., and Pfister G. Attractor reconstruction from filtered chaotic signals. Phys. Rev. A 41 (1990) 4151-4159
9. Dimitriadis, D., Maragos, P., Potamianos, A., 2002. Modulation features for speech recognition. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. I-377-I-380.
10. Duda R.O., Hart P.E., and Stork D.G. Pattern classification (2001), John Wiley & Sons, New York
11. Fletcher H. Speech and hearing in communication (1953), Van Nostrand, New York
12. Garofolo, J., Lamel, L., Fisher, W., Fiscus, J., Pallett, D., Dahlgren, N., Zue, V., 1993. Timit Acoustic-Phonetic Continuous Speech Corpus. Linguistic Data Consortium.
13. Gibson J.F., Farmer J.D., Casdagli M., and Eubank S. An analytic approach to practical state space reconstruction. Physica D 57 (1992) 1-30
14. Gold B., and Morgan N. Speech and audio signal processing (2000), John Wiley and Sons, New York
15. Hagen, A., Bourlard, H., Morris, A., 2001. Adaptive Ml-weighting in multi-band recombination of gaussian mixture ASR. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 257-260.
16. Hermansky, H., Tibrewala, S., Pavel, M., 1996. Towards ASR on partially corrupted speech. Fourth Internat. Conf. on Spoken Language (ICSLP) 461, pp. 462-465.
17. Indrebo, K.M., Povinelli, R.J., Johnson, M.T., 2003. A combined sub-band and reconstructed phase space approach to phoneme classification. ISCA Tutorial and Research Workshop on Non-linear Speech Processing (NOLISP), Le Croisic, France, pp. 107-110.
18. Isabelle, S.H., Oppenheim, A.V., Wornell, G.W., 1992. Effects of convolution on chaotic signals. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 133-136.
19. Johnson, M.T., Povinelli, R.J., Lindgren, A.C., Ye, J., Liu, X., Indrebo, K.M., in press. Time-domain isolated phoneme classification using reconstructed phase spaces. IEEE Trans. Speech Audio Process.
20. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Trans. Pattern Anal. Machine Intell. 20 (1998) 226-239
21. Kubin G. In: Kleijn W.B., and Paliwal K.K. (Eds). Nonlinear speech processing, speech coding and synthesis (1995), Elsevier Science
22. Lee K.-F., and Hon H.-W. Speaker-independent phone recognition using Hidden Markov models. IEEE Trans. Acoust. Speech Signal Process. 37 (1989) 1641-1648
23. Lindgren, A.C., 2003. Speech recognition using features extracted from phase space reconstructions. M.S. Thesis, Marquette University, Milwaukee, Wisconsin.
24. Lindgren, A.C., Johnson, M.T., Povinelli, R.J., 2003. Speech recognition using reconstructed phase space features. Internat. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, pp. 61-63.
25. Lindgren, A.C., Johnson, M.T., Povinelli, R.J., 2004. Joint frequency domain and reconstructed phase space features for speech recognition. Internat. Conf. on Acoustics, Speech and Signal Processing, Montreal, Canada, pp. I-533-I-536.
26. McCourt, P., Vaseght, S., Harte, N., 1998. Multi-resolution cepstral features for phoneme recognition across speech sub-bands. In: Proc. 1998 IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, 1998. ICASSP '98. Vol. 551, pp. 557-560.
27. Misra, H., Bourlard, H., Tyagi, V., 2003. New entropy based combination rules in Hmm/Ann multi-stream ASR. IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 741-744.
28. Moreno, A., Rutllan, M., 1996. Integrate polispectrum on speech recognition. Internat. Conf. on Spoken Language Processing, Philadelphia, pp. 1281-1284.
29. Nelder J.A., and Mead R. A simplex method for function minimization. Comput. J. 7 (1965) 308-313
30. Pitsikalis, V., Maragos, P., 2002. Speech analysis and feature extraction using chaotic models. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP). Vol. 531, pp. I-533-I-536.
31. Povinelli R.J., Bangura J.F., Demerdash N.A.O., and Brown R.H. Diagnostics of bar and end-ring connector breakage faults in polyphase induction motors through a novel dual track of time-series data mining and time-stepping coupled Fe-state space modeling. IEEE Trans. Energy Conversion 17 (2002) 39-46
32. Povinelli R.J., Johnson M.T., Lindgren A.C., and Ye J. Time series classification using gaussian mixture models of reconstructed phase spaces. IEEE Trans. Knowledge Data Eng. 16 (2004) 779-783
33. Roberts, F.M., Povinelli, R.J., Ropella, K.M., 2001. Identification of ECG arrhythmias using phase space reconstruction. Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg, Germany, pp. 411-423.
34. Sauer T., Yorke J.A., and Casdagli M. Embedology. J. Statist. Phys. 65 (1991) 579-616
35. Schafer G. A mathematical theory of evidence (1976), Princeton University Press
36. Takens, F., 1980. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick, pp. 366-381.
37. Teager, H.M., Teager, S.M., 1990. Evidence for nonlinear sound production mechanisms in the vocal tract. NATO ASI on Speech Production and Speech Modelling, pp. 241-261.
38. Tibrewala, S., Hermansky, H., 1997. Sub-band based recognition of noisy speech. IEEE Internat. Conf. on Acoustics, Speech and Signal Processing, Vol. 1252, pp. 1255-1258.
39. Wei, G., Chan, P.C.H., Tang, Z., Yu, J., Wang, L., 2002. Gas mixture analysis with dynamic gas sensor array signals. Second Internat. Symposium on Instrumentation Science and Technology, pp. 637-642.
40. Ye, J., Johnson, M.T., Povinelli, R.J., 2003. Phoneme classification over reconstructed phase space using principal component analysis. ISCA Tutorial and Research Workshop on Non-linear Speech Processing (NOLISP), Le Croisic, France, pp. 11-16.
41. Ye, J., Povinelli, R.J., Johnson, M.T., 2002. Phoneme classification using Naive Bayes classifier in reconstructed phase space. IEEE Signal Processing Society 10th Digital Signal Processing Workshop, p. 2.2.