By Raghunath S. Holambe
Advances in Non-Linear Modeling for Speech Processing contains complex themes in non-linear estimation and modeling innovations in addition to their functions to speaker popularity.
Non-linear aeroacoustic modeling process is used to estimate the real fine-structure speech occasions, which aren't printed through the fast time Fourier rework (STFT). This aeroacostic modeling method presents the impetus for the excessive answer Teager strength operator (TEO). This operator is characterised by means of a time answer that could music quick sign strength adjustments inside a glottal cycle.
The cepstral positive factors like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the value spectrum of the speech body and the section spectra is ignored. to beat the matter of neglecting the part spectra, the speech construction method should 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 rework demodulation (HTD) set of rules are mentioned.
Different gains derived utilizing above non-linear modeling ideas are used to boost a speaker identity procedure. eventually, it truly is proven that, the fusion of speech construction and speech notion mechanisms can result in a powerful characteristic set.
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Extra info for Advances in Non-Linear Modeling for Speech Processing
13) which is the product of squared amplitude and frequency. 2 Exponential Signal The exponentially decaying signal is modeled as x(t) = e−αt . Its Teager energy obtained using Eq. 14) Thus, the exponentially decaying signal has zero Teager energy. 3 AM Signal AM signal modeling is described in Sect. 1. 15) We can see that the Teager energy of an AM signal is composed by a term similar to the energy of a sinusoidal signal, and an oscillation scaled by the Teager Energy of the amplitude signal. 1 shows a sample AM signal (a) and its Teager energy (b).
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.
World Scientific, Singapore, pp 277–330 13. Teager HM, Teager SM (1989) Evidence for nonlinear sound production mechanisms in the vocal tract. In: Hardcastle W, Marchal A (eds) Speech production and speech modeling, vol 55. NATO Advanced Study Institute Series D, Bonas, France 14. Maragos P, Kaiser JF, Quatieri TF (1993) Energy separation in signal modulations with application to speech analysis. IEEE Trans Signal Process 41(10):3024–3051 15. Maragos P, Potamianos A (1999) Fractal dimensions of speech sounds: computation and application to automatic speech recogntion.
Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe