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Application of Wavelets in Speech Processing pp 41–46Cite as

Speech Recognition

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Abstract

The wavelet analysis can improve speech recognition performance through many approaches. First, it can be used to remove noise, and consequently the recognition process may perform better. Alternatively, wavelet-based features can be added to other successful features to improve recognition performance. Third, wavelets can serve as an activation function in neural-networks employed for speech recognition. Hybrid methodology may comprise a mix of one or more approaches.

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References

  1. Z. Tufekci, J.N. Gowdy, S. Gurbuz, E. Patterson, Applied mel-frequency discrete wavelet coefficients and parallel model compensation for noise-robust speech recognition. Speech Comm. 48(10), 1294–1307 (2006)

    Article  Google Scholar 

  2. O. Farooq, S. Datta, Wavelet-based denoising for robust feature extraction for speech recognition. Electron. Lett. 39(1), 163–165 (2003)

    Article  Google Scholar 

  3. M. Gupta, A. Gilbert, Robust speech recognition using wavelet coefficient features, Automatic speech recognition and understanding, IEEE automatic speech recognition and understanding workshop 2001 (ASRU'01), Madonna di Campiglio, Italy, 2001, pp. 445–448

    Google Scholar 

  4. B. Kotnik, Z. Kacic, B. Horvat, The usage of wavelet packet transformation in automatic noisy speech recognition systems. Int. Conf. Comput. Tool 2, 131–134 (2003)

    Google Scholar 

  5. C.C.E. de Abreu, M.A.Q. Duarte, F. Villarreal, An immunological approach based on the negative selection algorithm for real noise classification in speech signals. AEU-Int. J. Electron. C. 72, 125–133 (2017)

    Article  Google Scholar 

  6. R. Gomez, T. Kawahara, K. Nakadai, Optimized wavelet-domain filtering under noisy and reverberant conditions. APSIPA Trans. Signal Inf. Process 4(e3), 1–12 (2015)

    Google Scholar 

  7. A. Adiga, M. Magimai, C.S. Seelamantula, Gammatone wavelet cepstral coefficients for robust speech recognition, in 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), Xi’an, 2013, pp. 1–4

    Google Scholar 

  8. Z. Xueying, J. Zhiping, Speech recognition based on auditory wavelet packet filter. Proc. 7th Int. Conf. Signal Process. 2004 (ICSP '04) 1, 695–698 (2004)

    Google Scholar 

  9. O. Farooq, S. Datta, Mel filter-like admissible wavelet packet structure for speech recognition. IEEE Signal Process. Lett. 8(7), 196–198 (2001)

    Article  Google Scholar 

  10. J.N. Gowdy, Z. Tufekci, Mel-scaled discrete wavelet coefficients for speech recognition. Proc. IEEE Int. Conf. Acoust Speech Signal Process (ICASSP 2000) 3, 1351–1354 (2000)

    Google Scholar 

  11. P.K. Sahu, A. Biswas, A. Bhowmick, M. Chandra, Auditory ERB like admissible wavelet packet features for TIMIT phoneme recognition. Eng. Sci. Technol. Int. J. 17(3), 145–151 (2014)

    Article  Google Scholar 

  12. P.K. Astik Biswas, A.B. Sahu, M. Chandra, Feature extraction technique using ERB like wavelet sub-band periodic and aperiodic decomposition for TIMIT phoneme recognition. Int. J. Speech Technol. 17(4), 389–399 (2014)

    Article  Google Scholar 

  13. G. Choueiter, J. Glass, An implementation of rational wavelets and filter design for phonetic classification. IEEE Trans. Audio Speech Lang. Process. 15(3), 939–948 (2007)

    Article  Google Scholar 

  14. B. Rehmam, Z. Halim, G. Abbas, T. Muhammad, Artificial neural network- based speech recognition using DWT analysis applied on isolated words from oriental language. Malaysian J. Comput. Sci. 28(3), 242–262 (2015)

    Google Scholar 

  15. E. Pavez, J.F. Silva, Analysis and design of Wavelet-Packet Cepstral coefficients for automatic speech recognition. Speech Comm. 54(6), 814–835 (2012)

    Article  Google Scholar 

  16. V.T. Pham, Wavelet analysis for robust speech processing and applications, Ph.D. dissertation, Ph. D. thesis, (VDM Verlag, Saarbrücken, Germany, 2007), http://theses.eurasip.org/media/theses/documents/pham-van-tuan-wavelet-analysis-for-robust-speech-processing-and-applications.pdf

  17. S. Chang, Y. Kwon, S. Yang, I. Kim, Speech enhancement for nonstationary noise environment by adaptive wavelet packet. Proc. Int. Conf. Acoust. Speech Signal Process. (ICASSP 2002) 1, 561–564 (2002)

    Google Scholar 

  18. H.R. Tohidypour, S.A. Seyyedsalehi, H. Behbood, Comparison between wavelet packet transform, bark wavelet & MFCC for robust speech recognition tasks. Proc. 2nd Int. Conf. Ind. Mechatron. Autom. (ICIMA2010) 2, 329–332 (2010)

    Google Scholar 

  19. Z. Jie, L. Guo-liang, Z. Yu-zheng, L. Xiao-ying, A novel noise-robust speech recognition system based on adaptively enhanced bark wavelet MFCC. Proc. Sixth Int Conf Fuzzy Syst. Knowl. Discov., 2009 (FSKD '09) 4, 443–447 (2009)

    Google Scholar 

  20. H.R. Tohidypour, S.A. Seyyedsalehi, H. Behbood, H. Roshandel, A new representation for speech frame recognition based on redundant wavelet filter banks. Speech Comm. 54(2), 256–271 (2012)

    Article  Google Scholar 

  21. H.R. Tohidypour, A. Banitalebi-Dehkordi, Speech frame recognition based on less shift sensitive wavelet filter banks. SIViP 10(4), 633–637 (2016)

    Article  Google Scholar 

  22. S.G. Firooz, F. Almasganj, Y. Shekofteh, Improvement of automatic speech recognition systems via nonlinear dynamical features evaluated from the recurrence plot of speech signals. Comput. Electr. Eng. 58, 215–226 (2017)

    Article  Google Scholar 

  23. E. Avci, Z.H. Akpolat, Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Syst. Appl. 31(3), 495–503 (2006)

    Article  Google Scholar 

  24. L.D. Vignolo, D.H. Milone, H.L. Rufiner, Genetic wavelet packets for speech recognition. Expert Syst. Appl. 40(6), 2350–2359 (2013)

    Article  Google Scholar 

  25. Y. Shao, C.H. Chang, A generalized time-frequency subtraction method for robust speech enhancement based on wavelet filter bank modeling of human auditory system. IEEE Trans. Syst. Man Cybern. B Cybern. 37(4), 877–889 (2007)

    Article  Google Scholar 

  26. S. Yu, C.-H. Chang, Bayesian separation with Sparsity promotion in perceptual wavelet domain for speech enhancement and hybrid speech recognition. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(2), 284–293 (2011)

    Article  Google Scholar 

  27. P.N. Rajeswari, V. Sathyanarayana, Robust speech recognition using wavelet domain front end and Hidden Markov Models, in Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, ed. by V. Sridhar, H. Sheshadri, M. Padma, vol. 248, (Springer, New Delhi, 2014), pp. 435–442

    Google Scholar 

  28. Y. Wang, Z. Zhao, A noise-robust speech recognition system based on wavelet neural network, in Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science, ed. by H. Deng, D. Miao, J. Lei, F. L. Wang, vol. 7004, (Springer, Berlin, Heidelberg, 2011), pp. 392–397

    Google Scholar 

  29. X. Hu, L. Zhan, Y. Xue, W. Zhou, L. Zhang, Spoken arabic digits recognition based on wavelet neural networks, in 2011 IEEE International Conference on Systems, Man, and Cybernetics, (Anchorage, 2011), pp. 1481–1485

    Google Scholar 

  30. R. Ejbali, M. Zaied, C. Ben Amar, Wavelet network for recognition system of Arabic word. Int. J. Speech Technol. 13(3), 163–174 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering, Math and Physics, Cairo University, Faculty of Engineering, Giza, Egypt

    Mohamed Hesham Farouk

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  1. Mohamed Hesham Farouk
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Farouk, M.H. (2018). Speech Recognition. In: Application of Wavelets in Speech Processing. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-69002-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-69002-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69001-8

  • Online ISBN: 978-3-319-69002-5

  • eBook Packages: EngineeringEngineering (R0)

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