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

By Raghunath S. Holambe

Advances in Non-Linear Modeling for Speech Processing contains complex subject matters in non-linear estimation and modeling options besides their functions to speaker acceptance.

Non-linear aeroacoustic modeling process is used to estimate the real fine-structure speech occasions, which aren't printed by way of the fast time Fourier remodel (STFT). This aeroacostic modeling strategy presents the impetus for the excessive solution Teager strength operator (TEO). This operator is characterised via a time solution which may song speedy 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 significance spectrum of the speech body and the part spectra is ignored. to beat the matter of neglecting the part spectra, the speech construction approach could be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and on the spot frequency elements, the strength 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 innovations are used to boost a speaker id process. eventually, it really is proven that, the fusion of speech construction and speech notion mechanisms may end up in a strong function set.

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14) Ap 1 + ρp A new parameter set is obtained by taking the logarithm of the area ratio, yielding log area ratios (LAR). Since the LARs are derived from the LP coefficients, they are subject to the assumptions made in LP. To avoid singularity at |ρ p | = 1, an alternative for log area ratios are arcsin reflection coefficients [15], simply computed as taking inverse sine of the reflection coefficients. Perceptual linear prediction (PLP) is a form of generalized linear prediction that exploits some of the psychoacoustics principles, including critical band analysis, equal loudness pre-emphasis and the intensity loudness relationship [17].

Farmer JD, Sidorowich JJ (1988) Exploiting chaos to predict the future and reduce noise. In: Lee YC (ed) Evolution, learning, and cognition. 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.

In a scalar form, each element of the RBF nonlinear function in Eq. y j (x), 1 ≤ i ≤ I. 3 Truncated Taylor Series Approximation The analytical forms of nonlinear functions, such as the MLP and RBF described above, make the associated nonlinear dynamic systems difficult to analyze and make the estimation problems difficult to solve. Approximations are frequently used to gain computational simplifications while sacrificing accuracy for approximating the nonlinear functions. One very commonly used technique for the approximation is a truncated vector Taylor series expansion.

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