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Ill-condition enhancement for BC speech using RMC method

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Abstract

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis.

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References

  • Amino, K., Osanai, T., Kamada, H. T. Makinae, & Arai, T. (2011). Bone-conducted speech synthesis based on least squares method. In A. Neustein & H. Patil (Eds.), Forensic speaker recognition: Law enforcement and counter-terrorism (pp. 275–308).

  • Atal, B. S., & Hanauer, S. L. (1971). Speech analysis and synthesis by linear prediction of the speech wave. Journal of the Acoustical Society of America, 50(2), 637–655.

    Article  Google Scholar 

  • Bojanczyk, A. W.(1988). The QR decomposition of Toeplitz matrices. Asilomar Conference, 307–311.

  • Cheng, L., Dou, Y., Zhou, J., Wang, H., & Tao, L.(2023). Speaker-independent spectral enhancement for bone-conducted speech. Algorithms,16(153).

  • Demeure, C. J., & Scharf, L. L. (1990). Sliding windows and lattice algorithms for computing AR factors in the least squares theory of linear prediction. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(4), 721–725.

    Article  Google Scholar 

  • Kabal, P. (2003). Ill-conditioning and bandnwidth expansion in linear prediction of speech. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP) (pp. 824–827).

  • Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580.

    Article  Google Scholar 

  • Marple, S. L. (1990). A fast computational algorithm for the QR-like decomposition of the modified covariance method of linear prediction. In International conference on acoustics, speech, and signal processing.

  • Marple, L. (1980). A new autoregressive spectrum analysis algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 441–454.

    Article  Google Scholar 

  • Marple, S. L. (1991). A fast computational algorithm for the modified covariance method of linear prediction. Digital Signal Processing, 1(3), 124–133.

    Article  Google Scholar 

  • Martin, D. R., & Reichel, L. (2013). Minimization of functionals on the solution of a large-scale discrete ill-posed problem. BIT Numerical Mathematics, 53(1), 153–173.

    Article  MathSciNet  Google Scholar 

  • Ohidujjaman, Sugiura, Y., Shimamura, T., & Makinae, H. (2024). Packet loss concealment using regularized modified linear prediction through bone-conducted speech. In 2024 6th international conference on image, video and signal processing (IVSP 2024) (pp. 142–146).

  • Ohidujjaman, Sugiura, Y., Yasui, N., Shimamura, T., & Makinae, H. (2024). Regularized modified covariance method for spectral analysis of bone-conducted speech. Journal of Signal Processing, 28(3), 77–87.

  • Ohidujjaman, Yasui, N., Sugiura, Y., Shimamura, T., & Makinae, H.(2023). Packet loss compensation for voip through bone-conducted speech using modified linear prediction. IEEJ Transaction on Electrical and Electronic Engineering, 18(11), 1781–1790.

  • Paliwal, K. K., & Rao, P. V. S. (1981). A modified autocorrelation method of linear prediction for pitch-synchronous analysis of voiced speech. Signal Processing, 3(2), 181–185.

    Article  Google Scholar 

  • Rabiner, L. R., & Schafer, R. W. (2011). Theory and application of digital speech processing. Prentice-Hall.

    Google Scholar 

  • Rahman, M.S., & Shimamura, T. (2016). Pitch determination from bone conducted speech. IEICE Transactions on Information and Systems, E99-D, 283–287

  • Rahman, M. S., & Shimamura, T. (2019). Amplitude variation of bone-conducted speech compared with air-conducted speech. Acoustical Society of Japan, 40(5), 293–301.

    Google Scholar 

  • Rahman, M. A., Sugiura, Y., & Shimamura, T. (2017). Spectrum compensation method for speech signals based on prediction error filtering. WSEAS Transactions on Systems and Control, 12, 213–220.

    Google Scholar 

  • Rahman, M. A., Sugiura, Y., & Shimamura, T. (2017). Accurate power spectrum estimation of speech with spectrum compensation based on prediction error filtering. WSEAS Transactions on Signal Processing, 13, 21–25.

    Google Scholar 

  • Rialan, C. P., & Scharf, L L.(1988). Fast algorithms for computing QR and Cholesky factors of Toeplitz operators. IEEE Transactions on Acoustics Speech Signal Processing, 36(11), 1740–1748.

  • Wu, Y. (2012). Parametric inverse of severely ill-conditioned Hermitian matrices in signal processing. Journal of the Franklin Institute, 349(3), 1048–1060.

    Article  MathSciNet  Google Scholar 

  • Xingsheng, D., Liangbo, Y., Sichun, P., & Meiqing, D. (2015). An iterative algorithm for solving ill-conditioned linear least squares problems. Geodesy and Geodynamics, 6(6), 453–459.

    Article  Google Scholar 

  • Zhang, S., Sugiura, Y., & Shimamura, T. (2022). Bone-conducted speech synthesis based on least squares method. IEEJ Transactions on Electrical and Electronic Engineering, 17(3), 425–435.

    Article  Google Scholar 

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Acknowledgements

We sincerely express our gratitude to United International University (UIU) for support in making this research happen. This research was funded by the Institute for Advanced Research Publication Grant of United International University, Ref. No.: IAR-2024-Pub-058.

Funding

This research was funded by the Institute for Advanced Research Publication Grant of United International University, Ref. No.: IAR-2024-Pub-058.

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Authors

Contributions

Ohidujjaman: Writing -review & editing, Conceptualization, Formal analysis, Data curation. Mahmudul Hasan: Conceptualization, Formal analysis, Data curation. Shiming Zhang: Writing -review & editing, Data curation. Mohammad Nurul Huda: Conceptualization, Methodology. Mohammad Shorif Uddin: Supervision.

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Correspondence to Ohidujjaman or Mahmudul Hasan.

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Ohidujjaman, Hasan, M., Zhang, S. et al. Ill-condition enhancement for BC speech using RMC method. Int J Speech Technol 27, 1085–1092 (2024). https://doi.org/10.1007/s10772-024-10159-9

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  • DOI: https://doi.org/10.1007/s10772-024-10159-9

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