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Bark scaled oversampled WPT based speech recognition enhancement in noisy environments

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Abstract

The performance of speech recognition system degrades significantly in real-world environment, is a case of the acoustic mismatch between the training and operating conditions. This paper presents a two-stage approach to make a speech recognition system immune to additive and uncorrelated background noise i.e. robust. In the first stage, an oversampled wavelet packet decomposes the entire input noisy speech into seventeen nonlinear frequency subbands like the Bark scale of the human hearing system and the adaptive noise estimation based spectral subtraction filters the noisy speech from each subband signal. The oversampled WPT is linear and advantageous as it causes to overcome the shift-invariance complexity by removing the decimation after the filtering at each decomposition level. In the second stage, a nonparametric approach is used for feature extraction from filtered speech, and the parameters from the feature extraction stage are compared with the parameters extracted from speech signals stored in a template to recognize the utterance. A series of experiments are carried out to evaluate the performance of the proposed two-stage system in a variety of real environments, with and without the use of the first stage. Recognition accuracy is evaluated at the word level in a wide range of SNRs for various types of noisy environments. The experimental results show significant improvement in recognition performance at low SNR using the proposed system.

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References

  • Acero, A., & Stern, R. M. (1990). Environmental robustness in automatic speech recognition. In International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, USA (Vol. 2, pp. 849–852).

  • Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., et al. (2007). Automatic speech recognition and speech variability: A review. Speech Communication,49, 763–786.

    Article  Google Scholar 

  • Berouti, M., Schwartz, R., & Makhoul, J. (1979). Enhancement of speech corrupted by acoustic noise. In International Conference on Acoustics, Speech, and Signal Processing, Washington, DC, USA (Vol. 4, pp. 208–211).

  • Boll, S. F. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transaction on Speech and Audio Processing,27(2), 113–120.

    Google Scholar 

  • Cohen, I. (2003). Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging. IEEE Transactions on Speech, and Audio Processing,11(5), 466–475.

    Article  Google Scholar 

  • Cutajar, M., Gatt, E., Grech, I., Casha, O., & Micallef, J. (2013). Comparative study of automatic speech recognition techniques. IET Signal Processing,7(1), 25–46.

    Article  Google Scholar 

  • Flores, J. A. N. & Young, S. J. (1993). Adapting a HMM based recognizer for noisy speech enhanced by spectral subtraction. In European conference on speech communication and technology (pp. 829–832).

  • Gong, Y. (1995). Speech recognition in noisy environments: A survey. Computer Speech & Language,16, 261–291.

    MathSciNet  Google Scholar 

  • Hirsch, H. G. & Pearce, D. (2000). The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In International conference on spoken language processing, China, Oct 16–20, 2000 (pp. 17–21).

  • Juang, B. H. (1991). Speech recognition in adverse environments. Computer Speech & Language,5, 275–294.

    Article  Google Scholar 

  • Juang, B. H., & Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics,33(3), 251–272.

    Article  MathSciNet  Google Scholar 

  • Kamath, S., & Loizou, P. (2002). A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. In International conference on acoustics, speech, and signal processing, USA, May 2002 (Vol. 4, pp. 4160–4164).

  • Lin, L., Holmes, W., & Ambikairajah, E. (2002). Speech denoising using perceptual modification of Wiener filtering. Electronics Letters,38(23), 1486–1487.

    Article  Google Scholar 

  • Lin, L., Holmes, W. H., & Ambikairajah, E. (2003). Adaptive noise estimation algorithm for speech enhancement. Electronics Letters,39(9), 754–755.

    Article  Google Scholar 

  • Mallat, S. (1989). A theory for multi-resolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence,11(7), 674–693.

    Article  Google Scholar 

  • Mallat, S. (2009). A wavelet tour of signal processing: The sparse way (3rd ed.). New York: Academic Press.

    MATH  Google Scholar 

  • Martin, R. (2001). Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Transaction on Speech and Audio Processing,9(5), 504–512.

    Article  Google Scholar 

  • Olhede, S., & Walden, A. T. (2005). A generalized demodulation approach to time-frequency projections for multi-component signals. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,461, 2159–2179.

    Article  MathSciNet  Google Scholar 

  • Pallett, Devid S. (1985). Performance assessment of automatic speech recognizers. Journal of Research of the National Bureau of Standards,90(5), 371–385.

    Article  Google Scholar 

  • Rix, A. R., Beerends, J., Hollier, M., & Hekstra, A. (2001). Perceptual evaluation of speech quality (PESQ): A new method for speech quality assessment of telephone networks and codecs. In Proceedings of IEEE international conference on acoustics, speech, and signal processing, Salt Lake City, UT (Vol. 2, pp. 749–752).

  • Upadhyay, N., & Karmakar, A. (2014). A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments. International Journal of Speech Technology,17, 117–132.

    Article  Google Scholar 

  • Upadhyay, N., & Rosales, H. G. (2016). Auditory driven subband speech enhancement for automatic recognition of noisy speech. International Journal of Speech Technology,19(4), 869–880.

    Article  Google Scholar 

  • Walden, A. T., & Contreras, C. (1998). The phase-corrected undecimated discrete wavelet packet transform and its application to interpreting the timing of events. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,454, 2243–2266.

    Article  Google Scholar 

  • Yamada, Takeshi, Kumakura, Masakazu, & Kitawaki, Nobuhiko. (2006). Performance estimation of speech recognition system under noise conditions using objective quality measures and artificial voice. IEEE Transactions on Audio, Speech and Language Processing,14(6), 2006–2013.

    Article  Google Scholar 

  • Zwicker, E., & Terhardt, E. (1980). Analytical expressions for critical band rate and critical bandwidth as a function of frequency. The Journal of the Acoustical Society of America,68, 1523–1525.

    Article  Google Scholar 

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Correspondence to Navneet Upadhyay.

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Upadhyay, N., Rosales, H.G. Bark scaled oversampled WPT based speech recognition enhancement in noisy environments. Int J Speech Technol 23, 1–12 (2020). https://doi.org/10.1007/s10772-019-09657-y

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