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 contains complex issues in non-linear estimation and modeling innovations in addition to their purposes to speaker popularity.

Non-linear aeroacoustic modeling procedure is used to estimate the real fine-structure speech occasions, which aren't published by means of the fast time Fourier remodel (STFT). This aeroacostic modeling method presents the impetus for the excessive solution Teager strength operator (TEO). This operator is characterised by means of a time solution which can song swift sign power 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 significance spectrum of the speech body and the section 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 instant frequency elements, the power separation set of rules (ESA) and the Hilbert rework demodulation (HTD) set of rules are mentioned.

Different positive aspects derived utilizing above non-linear modeling concepts are used to strengthen a speaker id method. eventually, it really is proven that, the fusion of speech creation and speech conception 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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Bishop C (1997) Neural networks for pattern recognition. 1 Introduction We begin this chapter by discussing signal energy in general. , the Teager energy operator (TEO) and how it can be obtained by considering a second order differential equation, which describes the motion of an object suspended by a spring. This operator is interesting because it has a small time window, making it ideal for local (time) analysis of signals. The analysis of AM–FM signals using the Teager Energy Operator is probably the field where most of the research regarding the operator has been done so far.

This model can also be defined in the canonical form as discussed below. , o[N − 1])T ≡ vector of N observation samples. 10) 30 3 Linear and Dynamic System Model Fig. 1 The source-filter model of voice production for voiced and unvoiced speech [4] where o is the observation or measurement vector, with the dimensionality of N × 1, θ is the parameter vector, with dimensions of n × 1, H is the observation matrix, with dimensions of N × n and v is the observation noise vector, which is of dimensions N × 1.

Wiley, New York 12. Makhoul J (1975) Linear prediction: a tutorial review. In. Proceedings of the IEEE, vol 64, pp 561–580 13. Atal BS (1974) Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J Acoust Soc Am 55:1304–1312 14. Teager HM (1980) Some observations on oral air flow during phonation. IEEE Trans Speech Audio Process 28(5):599–601 15. Campbell J (1997) Speaker recognition: a tutorial. Proc IEEE 511(9):1437–1462 16.

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


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