Volume- 4
Issue- 3
Year- 2016
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Saeed Karimi
In the current system for the reconstruction of speech, it is used iterative reconstruction framework of short time Fourier transformation (STFT). When a lot of noise is added to the input speech, the iterative reconstruction framework of STFT can not properly reconstruct the input speech. In this paper, we offer a method that by using it, we will be able to extract the original signal from the noisy input signal. For achieving this goal, in each step of the iterative reconstruction of input speech, by taking a threshold value For magnitude spectrum, we remove the smaller amounts of threshold and for preserving the original signal features, we consider the phase spectrum then with combination of the modified magnitude spectrum information and the phase spectrum information, we reconstruction part of the signal in each iteration. Two experiments were examined. In the first experiment, we evaluated the reconstruction of input speech with changing the threshold value and in the second experiment, we evaluated the reconstruction of input speech with different numbers of iterations. The results showed that by using our method, when a lot of noise is added to the input speech, we can reconstruct the original signal very well.
[1] L. Liu, J. He, G. Palm, “Effects of phase on the perception of intervo-calic stop consonants,” Speech Communication, 1997, PP. 403-417.\
[2] J. W. Picone, “Signal modeling techniques in speech recognition,” IEEE Trans, 1993, PP. 1215-1247.
[3] P. L. Van Hove, M. H. Hayes, J. S. Lim, A. V Oppenheim, “Signal reconstruction from signed Fourier transform magnitude,” IEEE Trans. Acoust. Speech Signal Processing ASSP-31 (5), 1983, pp. 1286–1293.
[4] L. D. Alsteris, K. Paliwal, “Iterative reconstruction of speech from short-time Fourier transform phase and magnitude spectra,” Computer Speech and Language, 2007, PP.174–186.
[5] S. Wisdom, T. Powers, L. Atlas, and J. Pitton, “Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence,” arXiv:1509.00533 [cs, stat], Sep. 2015.
[6] K. Paliwal, B. Schwerin, K. Wojcicki, “Role of modulation magnitude and phase spectrum towards speech intelligibility,” proc. Speech Communication, 2011, PP. 327–339.
[7] D. W. Griffin, J. S. Lim, “Signal estimation from modified short-time Fourier transform,” IEEE Trans. Acoust. Speech Signal Processing ASSP-32 (2), 1984, PP. 236–243.
[8] S. H. Nawab, T. F. Quatieri, J. S. Lim, “Signal reconstruction from short-time Fourier transform magnitude,” IEEE Trans. Speech Signal Processing ASSP-31 (4), 1983, PP. 986–998.
[9] K. Paliwal, K. Wojcicki, B. Schwerin, “Single-channel speech enhan-cement using spectral subtraction in the short-time modulation domain,” Speech Comm. 52 (5), 2010b, PP. 450–475.
[10] P. Loizou, “Speech Enhancement: Theory and Practice,” Taylor and Francis, Boca Raton, FL, 2007.
[11] X. Huang, A. Acero, H. Hon, “Spoken Language Processing: A Guide to Theory, Algorithm, and System Development,” Prentice Hall, Upper Saddle River, New Jersey, 2001.
Department of Computer, Islamic Azad university, Dehloran Branch, iran, Tel NO 00988433720222
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