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Download PDF by Raghunath S. Holambe: 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 complex subject matters in non-linear estimation and modeling strategies besides their purposes to speaker attractiveness.

Non-linear aeroacoustic modeling process is used to estimate the $64000 fine-structure speech occasions, which aren't published by way of the quick time Fourier remodel (STFT). This aeroacostic modeling procedure offers the impetus for the excessive answer Teager strength operator (TEO). This operator is characterised via a time answer which may tune swift sign strength adjustments inside of a glottal cycle.

The cepstral beneficial properties like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the significance spectrum of the speech body and the section spectra is ignored. to beat the matter of neglecting the section spectra, the speech creation method could be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and immediate frequency parts, the strength separation set of rules (ESA) and the Hilbert rework demodulation (HTD) set of rules are mentioned.

Different good points derived utilizing above non-linear modeling suggestions are used to enhance a speaker id procedure. ultimately, it really is proven that, the fusion of speech construction and speech conception mechanisms can result in a strong function set.

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Extra resources for Advances in Non-Linear Modeling for Speech Processing

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2, a sample FM signal and it’s Teager energy is plotted. Here, the baseband signal is a pure sinusoidal signal. Notice that the output of the Teager Energy Operator in this case is a sinusoidal signal with the same frequency as the baseband signal. 1 Time Index Fig. 17) In Fig. 3, an AM–FM signal and it’s Teager energy is plotted. In this case, the operator’s tracking capabilities are not apparent, although we can spot some correlation between the peaks of the Teager energy and the zero-crossings of the AM–FM signal.

Academic Press, London 32. Birgmeier M (1995) A fully kalman-trained radial basis function network for nonlinear speech modeling. In: Proceedings of IEEE international conference on neural networks, (ICNN’95), Perth 33. Birgmeier M (1996) Nonlinear prediction of speech signals using radial basis function networks. In: EUSIPCO’96, vol 1, pp 459–462 34. de Maria FD, Figueiras AR (1995) Radial basis functions for nonlinear prediction of speech in analysis-by-synthesis coders. In: Proceedings of IEEE workshop on nonlinear signal and image processing, Halkidiki 35.

2 The vocal tract represented by concatenated lossless tubes of equal length but different cross-sectional area [4] by making approximations and assumptions regarding the acoustic properties of the vocal tract and the behavior of the sound wave in the process. The vocal tract shape is approximated as a concatenation of P rigid tubes each of constant diameter and equal length L/P, where L is the length of the vocal tract (typically 15–17 cm in adults). This is demonstrated in Fig. 2. The length of each segment needs to be approximately equal to the distance that sound travels in half a sample period [10, 11].

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


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