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Speech Intelligibility Based Enhancement System Using Modified Deep Neural Network and Adaptive Multi-band Spectral Subtraction

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Abstract

In contrast to the adverse environments, performances of existing speech enhancement algorithms do not always produce satisfactory results. In the case of worst signal to noise ratio, the processing is complicated and it may introduce signal distortions and degradation of intelligibility. To overcome the complexity of the existing speech enhancement algorithms, a hybrid concept for enhancing the speech quality and intelligibility is proposed in this research. The primary objectives of the research work is to increase the intelligibility of the speech enhancement system that has been trained for a particular speech signal using modified deep neural network (DNN) and adaptive multi-band spectral subtraction (AdMBSS). In this work, AdMBSS is used for enhancing the intelligibility of the speech signal using the additional phase information calculation, and finally, hybrid DNN and Nelder Mead optimization is utilized to improve the signal quality. Experimental results explain that the proposed framework achieves improved performance in signal to noise ratio, perceptual evaluation of signal quality and minimum mean square error. Finally, performances are taken for the more noises like bus noise, train noise, babble noise, airport noise, station noise and exhibition noise.

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References

  1. Hu, Y., & Loizou, P. C. (2007). Subjective comparison and evaluation of speech enhancement algorithms. Speech Communication,49(7), 588–601.

    Article  Google Scholar 

  2. Loizou, P. (2017). NOIZEUS: A noisy speech corpus for evaluation of speech enhancement algorithms. Speech Communication, 49, 588–601.

    Google Scholar 

  3. Hu, Y., & Loizou, P. C. (2008). Evaluation of objective quality measures for speech enhancement. IEEE Transactions on Audio, Speech and Language Processing,16(1), 229–238.

    Article  Google Scholar 

  4. Martin, R. (2005). Speech enhancement based on minimum mean-square error estimation and supergaussian priors. IEEE Transactions on Speech and Audio Processing,13(5), 845–856.

    Article  Google Scholar 

  5. Lotter, T., & Vary, P. (2005). Speech enhancement by MAP spectral amplitude estimation using a super-Gaussian speech model. EURASIP Journal on Applied Signal Processing,2005, 1110–1126.

    MATH  Google Scholar 

  6. Loizou, P. C. (2005). Speech enhancement based on perceptually motivated Bayesian estimators of the magnitude spectrum. IEEE Transactions on Speech and Audio Processing,13(5), 857–869.

    Article  Google Scholar 

  7. Loizou, P. C., & Kim, G. (2011). Reasons why current speech-enhancement algorithms do not improve speech intelligibility and) suggested solutions. IEEE Transactions on Audio, Speech and Language Processing,19(1), 47–56.

    Article  Google Scholar 

  8. Cohen, I. (2005). Relaxed statistical model for speech enhancement and a priori SNR estimation. IEEE Transactions on Speech and Audio Processing,13(5), 870–881.

    Article  Google Scholar 

  9. Ghanbari, Y., & Karami-Mollaei, M. R. (2006). A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets. Speech Communication,48(8), 927–940.

    Article  Google Scholar 

  10. Mohammadiha, N., Smaragdis, P., & Leijon, A. (2013). Supervised and unsupervised speech enhancement using nonnegative matrix factorization. IEEE Transactions on Audio, Speech and Language Processing,21(10), 2140–2151.

    Article  Google Scholar 

  11. Cohen, I. (2005). Speech enhancement using super-Gaussian speech models and noncausal a priori SNR estimation. Speech Communication,47(3), 336–350.

    Article  Google Scholar 

  12. Skowronski, M. D., & Harris, J. G. (2006). Applied principles of clear and Lombard speech for automated intelligibility enhancement in noisy environments. Speech Communication,48(5), 549–558.

    Article  Google Scholar 

  13. Taal, C. H., Hendriks, R. C., Heusdens, R., & Jensen, J. (2011). An algorithm for intelligibility prediction of time–frequency weighted noisy speech. IEEE Transactions on Audio, Speech and Language Processing,19(7), 2125–2136.

    Article  Google Scholar 

  14. Shao, Y., & Chang, C.-H. (2007). A generalized time–frequency subtraction method for robust speech enhancement based on wavelet filter banks modeling of the human auditory system. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),37(4), 877–889.

    Article  Google Scholar 

  15. Lu, Y., & Cooke, M. (2009). The contribution of changes in F0 and spectral tilt to increased intelligibility of speech produced in noise. Speech Communication,51(12), 1253–1262.

    Article  Google Scholar 

  16. Hansen, J. H., Radhakrishnan, V., & Arehart, K. H. (2006). Speech enhancement based on generalized minimum mean square error estimators and masking properties of the auditory system. IEEE Transactions on Audio, Speech and Language Processing,14(6), 2049–2063.

    Article  Google Scholar 

  17. Taghia, J., & Martin, R. (2014). Objective intelligibility measures based on mutual information for speech subjected to speech enhancement processing. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP),22(1), 6–16.

    Article  Google Scholar 

  18. Xu, Y., Du, J., Dai, L.-R., & Lee, C.-H. (2015). A regression approach to speech enhancement based on deep neural networks. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP),23(1), 7–19.

    Article  Google Scholar 

  19. Kim, G., & Loizou, P. C. (2010). Improving speech intelligibility in noise using environment-optimized algorithms. IEEE Transactions on Audio, Speech and Language Processing,18(8), 2080–2090.

    Article  Google Scholar 

  20. Jokinen, E., Takanen, M., Vainio, M., & Alku, P. (2014). An adaptive post-filtering method producing an artificial Lombard-like effect for intelligibility enhancement of narrowband telephone speech. Computer Speech & Language,28(2), 619–628.

    Article  Google Scholar 

  21. Petkov, P. N., Henter, G. E., & Kleijn, W. B. (2013). Maximizing phoneme recognition accuracy for enhanced speech intelligibility in noise. IEEE Transactions on Audio, Speech and Language Processing,21(5), 1035–1045.

    Article  Google Scholar 

  22. Tsao, Yu., & Lai, Y.-H. (2016). Generalized maximum a posteriori spectral amplitude estimation for speech enhancement. Speech Communication,76, 112–126.

    Article  Google Scholar 

  23. Chen, F. (2016). Predicting the intelligibility of noise-corrupted speech non-intrusively by across-band envelope correlation. Biomedical Signal Processing and Control,24, 109–113.

    Article  Google Scholar 

  24. Zorilă, T.-C., Stylianou, Y., Ishihara, T., & Akamine, M. (2016). Near and far field speech-in-noise intelligibility improvements based on a time–frequency energy reallocation approach. IEEE/ACM Transactions on Audio, Speech, and Language Processing,24(10), 1808–1818.

    Article  Google Scholar 

  25. Goehring, T., Bolner, F., Monaghan, J. J. M., Dijk, B. V., Zarowski, A., & Bleeck, S. (2017). Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users. Hearing Research,344, 183–194.

    Article  Google Scholar 

  26. Kolbæk, M., Tan, Z.-H., & Jensen, J. (2017). Speech intelligibility potential of general and specialized deep neural network based speech enhancement systems. IEEE/ACM Transactions on Audio, Speech, and Language Processing,25(1), 153–167.

    Article  Google Scholar 

  27. Loizou, P. C. (2013). Speech enhancement: Theory and practice. New York: CRC Press.

    Book  Google Scholar 

  28. Samui, S., Chakrabarti, I., & Ghosh, S. K. (2016). Improved single channel phase-aware speech enhancement technique for low signal-to-noise ratio signal. IET Signal Processing,10(6), 641–650.

    Article  Google Scholar 

  29. Ozaki, Y., Yano, M., & Onishi, M. (2017). Effective hyperparameter optimization using Nelder-Mead method in deep learning. IPSJ Transactions on Computer Vision and Applications, 9, 20.

    Article  Google Scholar 

  30. Hori, T., Chen, Z., Erdogan, H., Hershey, J. R., Roux, J. L., Mitra, V., et al. (2017). Multi-microphone speech recognition integrating beamforming, robust feature extraction, and advanced DNN/RNN backend. Berlin: Springer.

    Book  Google Scholar 

  31. An efficient MFCC extraction method in speech recognition

  32. Xu, Y., Du, J., Dai, L.-R., & Lee, C.-H. (2014). Global variance equalization for improving deep neural network based speech enhancement. In IEEE China summit & international conference on signal and information processing (China SIP), 2014 (pp. 71–75). IEEE.

  33. Zue, V., Seneff, S., & Glass, J. (1990). Speech database development at MIT: Timit and beyond. Speech Communication, 9(4), 351–356.

    Article  Google Scholar 

  34. Kavalekalam, S. M., Christensen, M. G., Gran, F., & Boldt, J. B. (2016). Kalman filter for speech enhancement in cocktail party scenarios using a codebook-based approach. In IEEE international conference on acoustics, speech and signal processing (ICASSP), 2016 (pp. 191–195). IEEE.

  35. Kirubagari, B., Palanivel, S., & Subathra, N. (2014). Speech enhancement using minimum mean square error filter and spectral subtraction filter. In International conference on information communication and embedded systems (ICICES), 2014 (pp. 1–7). IEEE.

  36. Hu, Y., & Loizou, P. C. (2003). A generalized subspace approach for enhancing speech corrupted by colored noise. IEEE Transactions on Speech and Audio Processing, 11(4), 334–341.

    Article  Google Scholar 

  37. Ephraim, Y., & Malah, D. (1985). Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(2), 443–445.

    Article  Google Scholar 

  38. Liu, B., Tao, J., Wen, Z., & Mo, F. (2016). Speech enhancement based on analysis-synthesis framework with improved parameter domain enhancement. Journal of Signal Processing Systems,82(2), 141–150.

    Article  Google Scholar 

  39. Hirsch, H. G., & Pearce, D. (2000). The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In ASR2000-automatic speech recognition: Challenges for the new Millenium ISCA tutorial and research workshop (ITRW).

  40. Rix, A. W., Beerends, J. G., Hollier, M. P., & Hekstra, A. P. (2001). Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221) (Vol. 2, pp. 749–752).

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Authors and Affiliations

  1. Electronics and Communication Engineering, Birla Institute of Technology, Mesra, India

    Tusar Kanti Dash & Sandeep Singh Solanki

  2. Electronics and Telecom Engineering, CV Raman College of Engineering, Bhubaneswar, India

    Tusar Kanti Dash

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  1. Tusar Kanti Dash
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  2. Sandeep Singh Solanki
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Correspondence to Tusar Kanti Dash.

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Dash, T.K., Solanki, S.S. Speech Intelligibility Based Enhancement System Using Modified Deep Neural Network and Adaptive Multi-band Spectral Subtraction. Wireless Pers Commun 111, 1073–1087 (2020). https://doi.org/10.1007/s11277-019-06902-0

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