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 themes in non-linear estimation and modeling recommendations 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 via the quick time Fourier rework (STFT). This aeroacostic modeling technique presents the impetus for the excessive solution Teager power operator (TEO). This operator is characterised by way of a time answer which may song swift sign strength 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 value spectrum of the speech body and the part spectra is ignored. to beat the matter of neglecting the part spectra, the speech creation procedure should 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 positive factors derived utilizing above non-linear modeling ideas are used to improve a speaker id approach. ultimately, it truly is proven that, the fusion of speech creation and speech belief mechanisms may end up in a strong characteristic set.

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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. Springer, Berlin 21. Ghahramani Z, Roweis S (1999) Learning nonlinear dynamic systems using an em algorithm. Adv Neural Inf Process Syst 11:1–7 22. Segall A (1976) Stochastic processes in estimation theory.

In this sense, we say that the state vector in the state-space model is hidden, and the state dynamics described by the state equation Eq. 19 is hidden dynamics. This is analogous to hidden Markov 36 3 Linear and Dynamic System Model model (HMM), where given an observation it is not possible to uniquely determine which (discrete) HMM state is responsible for generating that observation. 6 Time-Varying Dynamic System Model The linear state-space model defined earlier by Eqs. 20 is time invariant because the parameters that characterize this model do not change as a function of time k.

In: Proceedings of international conference on spoken language processing (ICSLP’96), pp 965–968 25. Kinnunen T (2003) Spectral features for automatic text-independent speaker recognition. D. thesis, Finland 26. Pickles J (1982) An introduction to the physiology of hearing. Academic Press, London 27. Fletcher H (1940) Auditory patterns. Rev Mod Phys 12:47–65 28. Quatieri TF (2004) Discrete-time speech signal processing. Principles and practice. Pearson Education, London 29. Gold B, Morgan N (2002) Speech and audio signal processing.

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