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 complex subject matters in non-linear estimation and modeling ideas besides their functions to speaker popularity.

Non-linear aeroacoustic modeling technique is used to estimate the $64000 fine-structure speech occasions, which aren't published by means of the quick time Fourier remodel (STFT). This aeroacostic modeling method offers the impetus for the excessive answer Teager strength operator (TEO). This operator is characterised via a time solution which could tune fast sign power alterations inside a glottal cycle.

The cepstral good points 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 missed. to beat the matter of neglecting the part spectra, the speech construction method will 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 strength separation set of rules (ESA) and the Hilbert rework demodulation (HTD) set of rules are mentioned.

Different beneficial properties derived utilizing above non-linear modeling ideas are used to strengthen a speaker id process. ultimately, it truly is proven that, the fusion of speech construction and speech notion mechanisms may end up in a strong characteristic set.

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

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By carefully choosing the number of tube segments, P, as explained in Eq. 11, any adverse effect of this approximation can be reduced. Using fewer tube segments has an adverse effect on the modeling whereas choosing the number of tube segments above the optimum number only affects the modeling if there is not enough speech data to estimate the parameters. The acoustic wave in the vocal tract is also assumed not to suffer any energy loss. The walls of the vocal tract are not rigid so acoustic energy is in fact lost due to the vibration of the walls and viscosity and turbulence of the airflow.

Rabiner LR, Juang BH (1993) Fundamentals of speech recognition. Prentice-Hall of India, New Delhi 2. Hansen J, Proakis J (2000) Discrete-time processing of speech signals, 2nd edn. IEEE Press, New York 3. Mammone R, Zhang X, Ramachandran R (1996) Robust speaker recognition: a feature based approach. IEEE Signal Process Mag 13:58–71 4. Gudnason J (2007) Voice source cepstrum processing for speaker identification. D. thesis, University of London 5. Dunn HK (1961) Methods of measuring vowel formant bandwidths.

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.

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