Advances in Nonlinear Speech Processing: 6th International by John Kane, Christer Gobl (auth.), Thomas Drugman, Thierry

By John Kane, Christer Gobl (auth.), Thomas Drugman, Thierry Dutoit (eds.)

This publication constitutes the lawsuits of the sixth foreign convention on Nonlinear Speech Processing, NOLISP 2013, held in Mons, Belgium, in June 2013. The 27 refereed papers incorporated during this quantity have been rigorously reviewed and chosen from 34 submissions. The paper are prepared in topical sections on speech and audio research; speech synthesis; speech-based biomedical functions; computerized speech acceptance; and speech enhancement.

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Pattern Recognition 45, 487–497 (2012) 19. : Reconstructing images from their most singular fractal manifold. IEEE Transactions on Image Processing 11, 345–350 (2002) 20. : The multi-fractal structure of contrast changes in natural images: from sharp edges to textures. Neural Computation 12, 763–793 (2000) 21. : Microcanonical multifractal formalism: a geometrical approach to multifractal systems. part 1: singularity analysis. Journal of Physics A: Mathematical and Theoretical 41, 015501 (2008) 22.

The method is described as follows. Let S(f ) be the narrow band spectrum of the speech frame s(t) (4 periods) and Avt (f ) the system representing its corresponding vocal tract transfer function. As usual, the derivative glottal signal dge (t) is extracted by analysis filtering according to DGe (f ) = S(f )/Av t(f ) (1) Following, a Rd candidate is used to generate an excitation sequence dgrd (t) of same length (Rd fixed, gain Ee = 1). The spectral envelopes Edge (f ) and Edgrd (f ) are estimated from dge (t) and dgrd (t) respectively using optimal TrueEnvelope estimation [8] in order to observe accurate H1 − H2 information.

1 Pitch Estimation in Clean Voiced Speech Figure 3 shows a clean voiced speech signal followed by its MP. The MP has a periodic structure and reveals extrema according to the glottal closure and opening instants. Fig. 3. a) Voiced clean speech. b) Its multi-scale product. a) illustrates the multi-scale product autocorrelation function of a clean voiced speech signal. The calculated function is obviously periodic and has the same period as the MP. The obtained ACMP shows one peak occurring at the pitch period.

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