Abstract
In this paper wavelet decomposition is used to decompose speech signal into five levels. Low-frequency part of the speech signal was reconstructed. Because different frequencies of the speech signal have different influence on the performance of the system, the acoustic model of each level was trained and tested. The experimental results show that the acoustic model of level 1 is the best for clean speech and the acoustic model of level 2 is the best for noisy speech .It is proved that the frequency band of A1 makes a lot of contribution on the performance of clean speech and the frequency band of A2 makes a lot of contribution on the performance of noisy speech.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Favero, R.F., King, R.W.: Wavelet Parameterization for Speech Recognition: Variations in Translation and Scale Parameters. In: Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN 1994., International Symposium on, April 13-16, vol. 2, pp. 694–697 (1994)
Tufekci, Z., Gowdy, J.N.: Feature Extraction Using Discrete Wavelet Transform for Speech Recognition. In: Southeastcon 2000. Proceedings of the IEEE, April 7-9, pp. 116–123 (2000)
Long, C.J., Datta, S.: Wavelet Based Feature Extraction for Phoneme Recognition. In: Spoken Language, 1996. ICSLP 1996. Proceedings., Fourth International Conference on, October 3-6, vol. 1, pp. 264–267 (1996)
Gupta, M., Gilbert, A.: Robust Speech Recognition Using Wavelet Coefficient Features. In: Automatic Speech Recognition and Understanding, 2001. ASRU 2001. IEEE Workshop on, December 9-13, pp. 445–448 (2001)
Donoho, D.L.: De-noising by soft-thresholding. Information Theory, IEEE Transactions on 41(3), 613–627 (1995)
Downie, T.R., Silverman, B.W.: The discrete multiple wavelet transform and thresholding methods, Signal Processing. IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on] 46(9), 2558–2561 (1998)
National High Technology Research and Development Program of China(HTRDP), http://www.863data.org.cn
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, L., Liu, G., Guo, J. (2005). A Study on Robustness of Large Vocabulary Mandarin Chinese Continuous Speech Recognition System Based on Wavelet Analysis. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_54
Download citation
DOI: https://doi.org/10.1007/11551188_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)