Noise suppression based on neurophysiologically-motivated SNR estimation for robust speech recognition

Advances in Neural Information Processing Systems

Volumn , Issue , 2001, Pages

  Tchorz, Jürgen ^a   Kleinschmidt, Michael ^a   Kollmeier, Birger ^a  

^a UNIVERSITY OF OLDENBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; FREQUENCY ESTIMATION; MAMMALS; MODULATION; SPEECH RECOGNITION;

ATTENUATING FREQUENCIES; DIGIT RECOGNITION EXPERIMENT; LOCAL SIGNAL-TO-NOISE RATIO; MODULATION FREQUENCIES; NOISE SUPPRESSION ALGORITHM; ROBUST SPEECH RECOGNITION; SPECTRAL SUBTRACTIONS; TEMPORAL CHARACTERISTICS;

SIGNAL TO NOISE RATIO;

EID: 84898984996     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Hermansky, H. and Morgan, N. (1994). RASTA processing of speech. IEEE Trans. Speech Audio Processing 2(4), pp. 578-589
2. Tchorz, J. and Kollmeier, B. (1999). A Model of Auditory Perception as Front End for Automatic Speech Recognition. J. Acoust. Soc. Am. 106, pp. 2040-2050
3. Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust., Speech, Signal Processing 27(2), pp. 113-120
4. Ephraim, Y. and Malah, M. (1984). Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Trans. Acoust., Speech, Signal Processing 32(6), pp. 1109-1121
5. Langner, G., Sams, M., Heil, P., and Schulze, H., (1997). Frequency and periodicity are represented in orthogonal maps in the human auditory cortex: evidence from magnetoencephalography. J. Comp. Physiol. A 181, pp. 665-676
6. Kollmeier, B. and Koch, R., (1994). Speech enhancement based on physiological and psychoacoustical models of modulation perception and binaural interaction. J. Acoust. Soc. Am. 95, pp. 1593-1602
7. Kasper, K., Reininger, H., Wolf, D., and Wüst, H. (1995). A speech recognizer with low complexity based on RNN. In: Neural Networks for Signal Processing V, Proc. of the IEEE workshop, Cambridge (MA), pp. 272-281
8. Kasper, K., Reininger, R., and Wolf, D. (1997). Exploiting the potential of auditory preprocessing for robust speech recognition by locally recurrent neural networks. Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) 2, pp. 1223-1227
9. Kleinschmidt, M., Tchorz, J., and Kollmeier, B. (2000). Combining speech enhancement and auditory feature extraction for robust speech recognition. Speech Communication, Special issue on robust ASR (accepted)
10. Ewert, S. and Dau, T. (1999). Frequency selectivity in amplitude-modulation processing. J. Acoust. Soc. Am. (submitted)
11. Tchorz, J. and Kollmeier, B. (2000). Estimation of the signal-to-noise ratio with amplitude modulation spectrograms. Speech Communication (submitted)
12. Dau, T., Püschel, D., and Kohlrausch, A. (1996). A quantitative model of the "effective" signal processing in the auditory system: II. Simulations and measurements. J. Acoust. Soc. Am 99, pp. 3623-3631
13. Dau, T., Kollmeier, B., and Kohlrausch, A. (1997). Modeling auditory processing of amplitude modulation: I. Modulation Detection and masking with narrowband carriers. J. Acoust. Soc. Am 102, pp. 2892-2905
14. Recommendation ITU-T G.729 Annex B, 1996