Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Recognition of Noise Quantity by Linear Predictive Coefficient of Speech Signal

음성신호의 선형예측계수에 의한 잡음량의 인식

  • Published : 2009.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

In order to reduce the noise quantity in a conversation under the noisy environment it is necessary for the signal processing system to process adaptively according to the noise quantity in order to enhance the performance. Therefore this paper presents a recognition method for noise quantity by linear predictive coefficient using a three layered neural network, which is trained using three kinds of speech that is degraded by various background noises. The performance of the proposed method for the noise quantity was evaluated based on the recognition rates for various noises. In the experiment, the average values of the recognition results were 98.4% or more for such noise using Aurora2 database.

잡음환경 하의 회화에서 잡음량을 줄이고 신호처리 시스뎀의 성능을 향상시키기 위해서는 잡음량에 따라서 적응적으로 처리되는 신호처리 시스템이 필요하다. 따라서 본 논문에서는 선형예측계수를 사용하여 잡음량을 인식하는 방법을 제안하며, 본 잡음량 인식은 다양한 배경잡음에 의하여 열화된 3종류의 음성이 신경회로망에 의하여 학습되어진다. 제안한 잡음량 인식의 성능은 다양한 잡음에 대하여 인식율을 사용하여 측정되었다. 본 실험에서는 Aurora2 데이터베이스를 사용하여 여러 잡음에 대하여 평균적으로 약 98.4% 이상의 양호한 인식결과를 확인할 수 있었다.

Keywords

References

  1. S. V. Vaseghi, B. P. Milner: Speech Recognition in Impulsive Noise. International Conference on Acoustics, Speech, and Signal Processing, pp. 437 -440, vol. 1, 1995
  2. O. Ichikawa, T. Fukuda, M. Nishimura: Local peak enhancement combined with noise reduction algorithms for robust automatic speech recognition in automobiles. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4869 - 4872, 2008
  3. J. T. Chien, L. M. Lee, and H. C. Wang, "Noisy speech recognition by using variance adapted hidden Markov models", IEE Electronics Letters, Vol. 31. No. 18, pp. 1555-1556, 1995. https://doi.org/10.1049/el:19951066
  4. K. K. Paliwal, "Neural net classifiers for robust speech recognition under noisy environments", IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 429-432, April 1990
  5. 최재승, "음성강조에의 응용을 위한 신경회로망에 의한 잡음량 추정법", 전자공학회 논문지 제42권 SP편 제3호, pp. 129-136, 2005
  6. W. G. Knecht, M. E. Schenkel, and G. S. Moschytz, "Neural network filters for speech enhancement", IEEE Trans. Speech and Audio Processing, Vol. 3, No. 6, pp. 433-438, 1995 https://doi.org/10.1109/89.482210
  7. S. Tamura, "An analysis of a noise reduction neural network", IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 89, No. 3, pp. 2001-2004, 1989
  8. P.B. Patil: Multilayered network for LPC based speech recognition. IEEE Transactions on Consumer Electronics, Vol. 44, No. 2, pp. 435 - 438, 1998 https://doi.org/10.1109/30.681960
  9. H. Hirsch and D. Pearce, "The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy conditions", in Proc. ISCA ITRW ASR2000 on Automatic Speech Recognition: Challenges for the Next Millennium, Paris, France, 2000
  10. D. Rumelhart, "Parallel Distributed Processing, vol. 1 and 2, MIT Press, Cambridge, MA, 1986
  11. S. K. Pal, S. Mitra, "Multilayer perceptron, fuzzy sets, and classification", IEEE Transaction on Neural Networks, vol. 3, no. 5, pp. 683-697, 1992 https://doi.org/10.1109/72.159058
  12. 윤태성, 심재성, "인간의 청각특성을 이용한 잡음 혼입시의 음성인식에 관한 연구", 창원대학교 산업기술연구소 논문집, 제6호, pp. 115-124, 1992
  13. Xueying Zhang, Yueling Guo, Xuemei Hou, "A speech Recognition Method of Isolated Words Based on Modified LPC Cepstrum", IEEE International Conference on Granular Computing, pp. 481-484, 2007