New PDF release: Advances in Non-Linear Modeling for Speech Processing

By Raghunath S. Holambe

ISBN-10: 1461415047

ISBN-13: 9781461415046

ISBN-10: 1461415055

ISBN-13: 9781461415053

Advances in Non-Linear Modeling for Speech Processing comprises complicated subject matters in non-linear estimation and modeling options in addition to their purposes to speaker acceptance.

Non-linear aeroacoustic modeling technique is used to estimate the real fine-structure speech occasions, which aren't printed by means of the quick 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 which may music quick sign strength alterations inside of a glottal cycle.

The cepstral positive aspects like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the importance spectrum of the speech body and the part spectra is overlooked. to beat the matter of neglecting the section spectra, the speech construction approach may 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 parts, the strength separation set of rules (ESA) and the Hilbert remodel demodulation (HTD) set of rules are mentioned.

Different positive factors derived utilizing above non-linear modeling suggestions are used to enhance a speaker id method. ultimately, it truly is proven that, the fusion of speech creation and speech notion mechanisms can result in a strong characteristic set.

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Additional info for Advances in Non-Linear Modeling for Speech Processing

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29], Lapedes et al. [35], Tishby et al. [36] and Wu et al. [37] have used the multi-layer perceptrons approach. Haykin et al. [38] and Wu et al. [39] have further discussed the recurrent neural net approach. Several non-parametric methods also play an important role such as, Lorenz’s method of analogues [40, 41] which may be the simplest of various nearest neighbor methods discussed by Farmer [27] and Yakowitz [42], which are further extended by Wu [37] as well as Gersho [43] such as nonlinear predictive vector quantization.

Tong H (1990) Non-linear time series—a dynamical system approach. Oxford University Press, Oxford 24. Papoulis A (1984) Probability, random variables and Stochastic processes. McGraw-Hill, New York 44 3 Linear and Dynamic System Model 25. Kantner M (1979) Lower bounds for nonlinear prediction error in moving avarage processes. Ann Prob 7(1):128–138 26. Casdagli M, jardins D, Eubank S, Farmer JD, Gibson J, Theiler J, Hunter N (1992) Nonlinear modeling of chaotic time series: theory and applications.

Even though it is possible to indefinitely increase the dimensionality of the signal vectors in the linear model so as to achieve the same mathematical description as a dynamic system model, it increases the computations associated with the model. Furthermore, according to the signal generation mechanisms, speech signals are endowed with hidden signal dynamics, which can be represented in much more natural way by dynamic system model rather than its high-dimensional linear model counterpart. Dynamic system models typically have two essential characteristics.

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


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