Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Adaptation of Classification Model for Improving Speech Intelligibility in Noise

음성 명료도 향상을 위한 분류 모델의 잡음 환경 적응

  • Jung, Junyoung (School of Electrical Engineering, Soongsil University) ;
  • Kim, Gibak (School of Electrical Engineering, Soongsil University)
  • Received : 2018.03.16
  • Accepted : 2018.05.08
  • Published : 2018.07.30
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Abstract

This paper deals with improving speech intelligibility by applying binary mask to time-frequency units of speech in noise. The binary mask is set to "0" or "1" according to whether speech is dominant or noise is dominant by comparing signal-to-noise ratio with pre-defined threshold. Bayesian classifier trained with Gaussian mixture model is used to estimate the binary mask of each time-frequency signal. The binary mask based noise suppressor improves speech intelligibility only in noise condition which is included in the training data. In this paper, speaker adaptation techniques for speech recognition are applied to adapt the Gaussian mixture model to a new noise environment. Experiments with noise-corrupted speech are conducted to demonstrate the improvement of speech intelligibility by employing adaption techniques in a new noise environment.

본 논문에서는 잡음 환경의 음성 신호를 시간-주파수 영역으로 분해한 후 0 또는 1로 표현되는 이진 마스크를 적용하여 음성의 명료도를 높이는 방법에 대해 다룬다. 시간-주파수 영역으로 분해된 신호에 대해 상대적으로 잡음이 많이 섞인 경우는 마스크 "0"을 할당하여 제거하고, 그렇지 않은 경우는 마스크 "1"을 할당하여 보존하는 방식을 채택한다. 이러한 이진 마스크의 추정은 가우시안 혼합 모델로 학습된 베이지안 분류기를 사용한다. 가우시안 혼합 모델 학습에 포함된 잡음 환경에 대해서는 학습된 모델을 이용하여 추정된 이진 마스크의 적용을 통해 잡음 환경에서 음성 명료도를 높일 수 있으나 학습에 포함되지 않은 잡음 환경에 대해서는 음성 명료도를 향상시키지 못하는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위해 학습 모델을 잡음 환경에 적응시키고자 한다. 새로운 잡음 환경에 대처하고자 음성 인식에서 사용되는 대표적인 화자 적응 방법을 적용하였으며 실험을 통해 새로운 잡음 환경에 적응함을 확인하였다.

Keywords

References

  1. Y. Hu and P. Loizou, "Subjective comparison and evaluation of speech enhancement algorithms," Speech communication, vol. 49, no. 7, pp. 588-601, Jul. 2007. https://doi.org/10.1016/j.specom.2006.12.006
  2. Y. Hu and P. Loizou, "Evaluation of objective quality measures for speech enhancement," IEEE Transactions on Speech and Audio Processing, vol. 16, no. 1, pp. 229-238, 2008. https://doi.org/10.1109/tasl.2007.911054
  3. Y. Hu and P. Loizou, "A comparative intelligibility study of single-microphone noise reduction algorithms." The Journal of the Acoustical Society of America, vol. 122, no. 3, p. 1777, Sep. 2007. https://doi.org/10.1121/1.2766778
  4. G. Brown and M. Cooke, "Computational auditory scene analysis," Computer speech and language, vol. 8, pp. 297-336, 1994 https://doi.org/10.1006/csla.1994.1016
  5. G. Brown and M. Cooke, "Computational auditory scene analysis," Computer speech and language, vol. 8, pp. 297-336, 1994 https://doi.org/10.1109/9780470043387
  6. D. Wang, "On ideal binary mask as the computational goal of auditory scene analysis," In Divenyi P. (ed.), Speech Separation by Humans and Machines, pp. 181-197, Kluwer Academic, Norwell MA, 2005. https://doi.org/10.1007/0-387-22794-6_12
  7. G. Kim. Y. Lu, Y. Hu and P. Loizou, "An algorithm that improves speech intelligibility in noise for normal-hearing listeners," The Journal of the Acoustical Society of America, vol. 126, no. 3, pp 1486-1494, 2009. https://doi.org/10.1121/1.3184603
  8. Y. Hu, P. Loizou, "Environment-specific noise suppression for improved speech intelligibility by cochlear implant users", The Journal of the Acoustical Society of America, vol. 127, no. 6, pp 3689-3695, 2010. https://doi.org/10.1121/1.3365256
  9. K. Han, D. Wang, "A classification based approach to speech segregation", The Journal of the Acoustical Society of America, vol. 132, no. 5, pp 3475-3483, 2012. https://doi.org/10.1121/1.4754541
  10. Y. Wang, K. Han, D. Wang, "Exploring monaural features for classification-based speech segregation", IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 2, pp 270-279, 2013. https://doi.org/10.1109/tasl.2012.2221459
  11. G. Kim, "A Post-processing for Binary Mask Estimation Toward Improving Speech Intelligibility in Noise," Journal of Broadcast Engineering, Vol. 18, No.2, pp.311-318, March, 2013. https://doi.org/10.5909/jbe.2013.18.2.311
  12. G. Kim, P. Loizou, "Improving speech intelligibility in noise using environment optimized algorithms", IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 8, pp 2080-2090, 2010. https://doi.org/10.1109/tasl.2010.2041116
  13. G. Kim, "Eigenvoice Adaptation of Classification Model for Binary Mask Estimation," Journal of Broadcast Engineering, Vol.20, No.1, pp.164-170, 2015. https://doi.org/10.5909/jbe.2015.20.1.164
  14. R. Kuhn, J. Junqua, P. Nguyen, N. Niedzielski, "Rapid Speaker Adaptation in Eigenvoice Space," IEEE Transactions on Speech and Audio Proceeding, vol. 8, no. 6, pp. 695-707, November 2000. https://doi.org/10.1109/89.876308
  15. D. Povey and G. Saon, "Feature and model space speaker adaptation with full covariance Gaussians," Proceedings of Interspeech, USA, september 2006.
  16. K. Shinoda, "Speaker Adaptation Techniques for Speech Recognition Using Probabilistic Models," Electronics and Communications in Japan, Part 3, Vol. 88, No. 12, pp.25-42, 2005. https://doi.org/10.1002/ecjc.20207
  17. J. Tchorz and B. Kollmeier, "Estimation of the signal-to-noise ratio with amplitude modulation spectrograms," Speech Communication, vol. 38, no. 1-2, pp. 1-17, Sep. 2002. https://doi.org/10.1016/s0167-6393(01)00040-1
  18. J. Tchorz and B. Kollmeier, "SNR estimation based on amplitude modulation analysis with applications to noise suppression," IEEE Transactions on Speech and Audio Processing, vol. 11, no. 3, pp. 184-192, May 2003. https://doi.org/10.1109/tsa.2003.811542
  19. IEEE, "IEEE recommended practice for speech quality measurements," IEEE Trans. Audio Electroacoust., vol. 17, pp. 225-246, 1969. https://doi.org/10.1109/tau.1969.1162058
  20. A. Varga and H. J. M. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems," Speech Communication, vol. 12, pp. 247-251, 1993. https://doi.org/10.1016/0167-6393(93)90095-3