Filtering the time sequences of spectral parameters for speech recognition

Speech Communication

Volumn 22, Issue 4, 1997, Pages 315-332

  Nadeu, Climent ^a   Pachès Leal, Pau ^a   Juang, Biing Hwang ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b LUCENT TECHNOLOGIES   (United States)

Author keywords

Hidden Markov model (HMM); Linear filter; Modulation frequency; Spectral estimation; Speech recognition

Indexed keywords

MATHEMATICAL MODELS; SIGNAL FILTERING AND PREDICTION; SPEECH ANALYSIS;

FREQUENCY ANALYSIS; TIME SEQUENCES OF SPECTRAL PARAMETERS (TSSP);

SPEECH RECOGNITION;

EID: 0031221099     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-6393(97)00030-7     Document Type: Article

References (31)

1. Applebaum, T.H., Hanson, B., 1990. Robust speaker-independent word recognition using spectral smoothing and temporal derivatives. Proc. EUSIPCO'90, Barcelona, pp. 1183-1186.
2. Arai, T., Pavel, M., Hermansky, H., Avendaño, C., 1996. Intelligibility of speech with filtered time trajectories of spectral envelopes. Proc. ICSLP'96, Philadelphia, pp. 2490-2493.
3. Avendaño, C., van Vuuren, S., Hermansky, H., 1996. Data based filter design for RASTA-like channel normalization in ASR. Proc. ICSLP'96, Philadelphia, pp. 2087-2090.
4. Bonafonte, A., Estany, R., Vives, E., 1995. Study of subword units for spanish speech recognition. Proc. Eurospeech'95, Madrid, pp. 1607-1610.
5. Drullman, R., Festen, J.M., Plomp, R., 1994a. Effect of temporal envelope smearing on speech reception. J. Acoust. Soc. Amer. 95 (2), 1053-1064.
6. Drullman, R., Festen, J.M., Plomp, R., 1994b. Effect of reducing slow temporal modulations speech reception. J. Acoust. Soc. Amer. 95 (5), 2670-2680.
7. Furui, S., 1986. Speaker-independent isolated word recognition using dynamic features of speech spectrum. IEEE Trans. Acoust. Speech Signal Process. 34 (1), 52-59.
8. Greenberg, S., Kingsbury, B., 1997. The modulation spectrogram: in pursuit of an invariant representation of speech. Proc. Internat. Conf. Acoust. Speech Signal Process.'97, Munich, pp. 1647-1650.
9. Haeb-Umbach, R., Geller, D., Ney, H., 1993. Improvements in connected digit recognition using linear discriminant analysis and mixture densities. Proc. Internat. Conf. Acoust. Speech Signal Process.'93, Minneapolis, pp. 239-242.
10. Hanson, B.A., Applebaum, T.H., Junqua, J.C., 1996. Spectral dynamics for speech recognition under adverse conditions. In: Lee, C.-H., Soong, F.K. (Eds.), Advanced Topics in Automatic Speech and Speaker Recognition, Kluwer Academic Publishers, Dordrecht.
11. Hermansky, H., Morgan, N., 1994. RASTA processing of speech. IEEE Trans. Speech Audio Process. 2 (4), 578-589.
12. Hermansky, H., Avendaño, C., van Vuuren, S., Tibrewala, S., 1997. Recent advances in addressing sources of non-linguistic information. Proc. ESCA-NATO Workshop on Robust Speech Recognition for Unknown Communication Channels, Pont-a-Mousson (France), pp. 103-106.
13. Hirsch, H.G., Meyer, P., Ruehl, H., 1991. Improved speech recognition using high-pass filtering of spectral envelopes. Proc. EUROSPEECH'91, Genoa, pp. 413-416.
14. Houtgast, T., Steeneken, H.J.M., 1985. A review of the MTF concept in room acoustics and its use for estimating speech intelligibility in auditoria. J. Acoust. Soc. Amer. 77 (3), 1069-1077.
15. Juang, B.H., Rabiner, L.R., Wilpon, J.G., 1987. On the use of bandpass liftering in speech recognition. IEEE Trans. Acoust. Speech Signal Process. 35 (7), 947-953.
16. 0-Sinh window for spectrum analysis. IEEE Trans. Acoust. Speech Signal Process. 28, 105-107.
17. Katagishi, K., Singer, H., Aikawa, K., Sagayama, S., 1993. Feature extraction using a matrix coefficient filter for speech recognition. Speech Communication 13 (3-4), 297-306.
18. Lee, C.-H., Giachin, E., Rabiner, L.R., Pieraccini, R., Rosenberg, A.E., 1992. Improved acoustic modelling for large vocabulary CSR. Computer Speech and Language 6, 103-127.
19. Leonard, R.G., 1984. A database for speaker-independent digit recognition. Proc. Internat. Conf. Acoust. Speech Signal Process.'84, pp. 42.11.1-4.
20. Moreno, A., et al., 1993. ESPRIT Project: Speech Technology Assessment in Multilingual Applications (SAM-A). Document SAM-A/6002.
21. Nadeu, C., Juang, B.H., 1994. Filtering spectral parameters for speech recognition. Proc. ICSLP'94, Yokohama, pp. 1927-1930.
22. Nadeu, C., Pachès-Leal, P., Juang, B.H., 1995. On the decorrelation of filter-bank energies in speech recognition. Proc. Eurospeech'95, Madrid, pp. 1381-1384.
23. Nadeu, C., Mariño, J.B., Hernando, J., Nogueiras, A., 1996. Frequency and time filtering of filter-bank energies for HMM speech recognition. Proc. ICSLP'96, Philadelphia, pp. 2348-2351.
24. Oppenheim, A.V., Schafer, R.W., 1989. Discrete-Time Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, 1989.
25. Pachès-Leal, P., Nadeu, C., 1996. On parameter filtering in continuous subword-unit-based speech recognition. Proc. ICSLP'96, Philadelphia, pp. 1065-1068.
26. Picone, J.W., 1993. Signal modeling techniques in speech recognition. Proc. of the IEEE 81 (9), 1215-1247.
27. Rabiner, L.R. Juang, B.H., 1993. Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs, NJ, 1993.
28. Rosenberg, A.E., Lee, C.-H., Soong, F.K., 1994. Cepstral channel normalization techniques for HMM-based speaker verification. Proc. ICSLP'94, Yokohama, pp. 1835-1838.
29. Slepian, D., 1978. Prolate spheroidal wave functions, Fourier analysis, and uncertainty - V: The discrete case. The Bell System Tech. Journal 57 (5), 1371-1430.
30. Tohkura, Y., 1987. A weighted cepstral distance measure for speech recognition. IEEE Trans. Acoust. Speech Signal Process. 35 (10).
31. Wilpon, J., Lee, C.-H., Rabiner, L.R., 1993. Connected digit recognition based on improved acoustic resolution. Computer, Speech and Language 7 (1), 15-26.