Download Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe PDF

By Raghunath S. Holambe

Advances in Non-Linear Modeling for Speech Processing comprises complicated issues in non-linear estimation and modeling concepts besides their purposes to speaker popularity.

Non-linear aeroacoustic modeling technique is used to estimate the real fine-structure speech occasions, which aren't printed through the quick time Fourier rework (STFT). This aeroacostic modeling strategy presents the impetus for the excessive answer Teager power operator (TEO). This operator is characterised through a time solution which could music fast sign power alterations inside a glottal cycle.

The cepstral gains like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the value spectrum of the speech body and the part spectra is ignored. to beat the matter of neglecting the section spectra, the speech creation process could be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and prompt frequency elements, the power separation set of rules (ESA) and the Hilbert remodel demodulation (HTD) set of rules are mentioned.

Different positive aspects derived utilizing above non-linear modeling ideas are used to boost a speaker identity approach. eventually, it truly is proven that, the fusion of speech creation and speech belief mechanisms may end up in a strong function set.

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Some important aspects of physical modeling of speech production system like vocal fold oscillations, the turbulent sound source, aerodynamics observations regarding nonlinear interactions between the air flow and the acoustic field are discussed in this chapter. 24 2 Nonlinearity Framework in Speech Processing References 1. Haykin S (2001) Adaptive filter theory. Prentice Hall, Upper Saddle River 2. Kubin G (1995) Nonlinear processing of speech. In: Kleijn WB, Paliwal KK (eds) Speech coding and synthesis.

Thesis, Finland 26. Pickles J (1982) An introduction to the physiology of hearing. Academic Press, London 27. Fletcher H (1940) Auditory patterns. Rev Mod Phys 12:47–65 28. Quatieri TF (2004) Discrete-time speech signal processing. Principles and practice. Pearson Education, London 29. Gold B, Morgan N (2002) Speech and audio signal processing. Wiley, New York 30. Moore BCJ, Glasberg BR (1996) A revision of Zwickers loudness model. Acustica–Acta Acustica 82:335–345 31. Zwicker E, Terhardt E (1980) Analytical expressions for critical band rate and critical bandwidth as a function of frequency.

L As another example, for the RBF nonlinearity Eq. 33 or Eq. 37 with Gaussian kernels, the (i, l)th element of the Jacobian matrix is J Jil = Wi j . ex p − (x − c j )T ⎩ 2 −1 j ⎫⎡ ⎤ ⎬ −1 (x − c j ) ⎣ (x − c j )⎦ , ⎭ j for 1 ≤ i ≤ I, 1 ≤ l ≤ L . 41) If the kernel function of Eq. (αl +1)( x 2 +α 2 )−α−1 xl for 1 ≤ i ≤ I, 1 ≤ l ≤ L . 4 Quasi-Linear Approximation The linear Taylor series approximation discussed above requires evaluation of the Jacobian matrix in an analytical form. If such a form is not available, or some or all elements of the Jacobian matrix do not exist as in the case where discontinuity exists, then a linear approximation to a nonlinear function can be accomplished by the quasi-linear approximation method, which we discuss now.

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