Advances in Non-Linear Modeling for Speech Processing - download pdf or read online

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 themes in non-linear estimation and modeling strategies besides their functions to speaker attractiveness.

Non-linear aeroacoustic modeling method is used to estimate the $64000 fine-structure speech occasions, which aren't printed through the fast time Fourier rework (STFT). This aeroacostic modeling strategy presents the impetus for the excessive answer Teager strength operator (TEO). This operator is characterised by way of a time solution which could song quick sign strength alterations inside a glottal cycle.

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

Different beneficial properties derived utilizing above non-linear modeling recommendations are used to increase a speaker id process. eventually, it really is proven that, the fusion of speech creation and speech notion mechanisms may end up in a powerful characteristic set.

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

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IEEE Trans Acoustic Speech Signal Process 34:744–754 35. Ananthapadmanabha TV, Fant D (1982) Calculation of true glottal flow and its components. Speech Commun 1:167–184 36. Jankowski CR (1996) Signal processing using the Teager energy operator and other nonlinear operators. D. thesis. MIT, Cambridge 37. Titze I (1988) The physics of small-amplitude oscillation of the vocal folds. J Acoust Soc Amer 83:1536–1552 38. Titze I (1994) Principles of voice production. Prentice-Hall, Englewood Cliffs 39.

1007/978-1-4614-1505-3_3, © The Author(s) 2012 27 28 3 Linear and Dynamic System Model The coefficients ak are referred to as the linear prediction coefficients and their estimation is termed as linear prediction analysis. Quantization of these coefficients, or of a transformed version of these coefficients, is called linear prediction coding (LPC) which is useful in speech coding. Here, the linear prediction coefficients are used as linear weights for the past samples and e[n] is additive with unity weight.

Proc IEEE 511(9):1437–1462 16. Honda K (2008) Physiological processes of speech production. Springer, Berlin 17. Hermansky H (1990) Perceptual linear prediction analysis for speech. J Acoust Soc Am 87:1738–1752 18. Rosenberg AE, Sambur MR (1975) New techniques for automatic speaker verification. IEEE Trans Acoust Speech Signal Process 23(2):169–176 19. Deng L, O’Shaughnessy D (2003) Speech processing a dynamic and optimization-oriented approach. Marcel Dekker, New York 20. Tanizaki H (1996) Nonlinear filters—estimation and applications, 2nd edn.

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


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