DOI QR코드

DOI QR Code

음성 및 잡음 인식 알고리즘을 이용한 환경 배경잡음의 제거

Reduction of Environmental Background Noise using Speech and Noise Recognition

  • 투고 : 2010.12.14
  • 심사 : 2011.01.03
  • 발행 : 2011.04.30

초록

본 논문에서는 먼저 신경회로망의 학습에 오차역전파 학습 알고리즘을 사용하여 각 프레임에서의 음성 및 잡음 구간의 검출에 의한 음성인식 알고리즘을 제안한다. 그리고 신경회로망에 의하여 음성 및 잡음 구간의 검출에 따라서 각 프레임에서 잡음을 제거하는 스펙트럼 차감법을 제안한다. 본 실험에서는 제안한 음성인식알고리즘의 성능을 원음성에 백색잡음 및 자동차 잡음을 부가하여 인식율을 평가한다. 또한 인식시스템에 의하여 검출된 음성 및 잡음 구간을 이용하여 각 프레임에서의 스펙트럼 차감법에 의한 잡음제거의 실험결과를 나타낸다. 잡음에 의하여 오염된 음성에 대하여 신호대잡음비를 사용하여 본 알고리즘이 유효하다는 것을 확인한다.

This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame using a neural network training by back-propagation algorithm, then proposes the spectral subtraction method which removes the noises at each frame according to detection of the speech and noise sections. In this experiment, the performance of the proposed recognition system was evaluated based on the recognition rate using various speeches that are degraded by white noise and car noise. Moreover, experimental results of the noise reduction by the spectral subtraction method demonstrate using the speech and noise sections detecting by the speech recognition algorithm at each frame. Based on measuring signal-to-noise ratio, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise using signal-to-noise ratio.

키워드

참고문헌

  1. Simpson, et. al., "Spectral Enhancement to Improve the Intelligibility of Speech in Noise for Hearing Impaired Listeners," Acta Otolaryngol, Suppl. 469, pp. 101-107, 1990.
  2. J.P. Haton, "Automatic recognition of noisy speech," In A.J.R. Ayuso and J.M.L. Soler, Eds., Speech Recognition and Coding-New Advances and Trends, Springer Verlag, Berlin, Germany, pp.3-13, 1995.
  3. S.F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Processing. Vol.27, No.2, pp. 113-120, 1979. https://doi.org/10.1109/TASSP.1979.1163209
  4. R. Martin, "Speech Enhancement Based on Minimum Mean-Square Error Estimation and Supergaussian Priors," IEEE Transactions on Speech and Audio Processing, Vol.13, No.5, pp. 845-856, 2005. https://doi.org/10.1109/TSA.2005.851927
  5. 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.
  6. 최재승, "신경회로망에 의한 음성 및 잡음 인식시스템," 한국전자통신학회논문지, 제5권 4호, pp.357-362, 2010.
  7. T.T. Le, J.S. Mason and T. Kitamura, "Characteristics of multi-layer perceptron models in enhancing degraded speech," Proc. ICSLP-94, pp.1611-1614, 1994.
  8. D.E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagation errors," Nature, 323, pp. 533-536, 1986. https://doi.org/10.1038/323533a0
  9. 최재승, "FFT 켑스트럼을 사용한 배경잡음의 제거," 한국해양정보통신학회 추계학술대회 논문집, 14권 2호, pp.264-267, 2010.
  10. J. He, L. Liu, and G. Palm, "On the use of residual cepstrum in speech recognition," IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.1, pp.5-8, 1996.

피인용 문헌

  1. Sliding Window based Sensor Data Processing in IoT Environment vol.21, pp.4, 2011, https://doi.org/10.9728/dcs.2020.21.4.825