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© 2012

Advances in Non-Linear Modeling for Speech Processing

Book

Part of the SpringerBriefs in Electrical and Computer Engineering book series

Also part of the SpringerBriefs in Speech Technology book sub series (BRIEFSSPEECHTECH)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 1-9
  3. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 11-25
  4. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 27-44
  5. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 45-59
  6. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 61-75
  7. Raghunath S. Holambe, Mangesh S. Deshpande
    Pages 77-99
  8. Back Matter
    Pages 101-102

About this book

Introduction

Advances in Non-Linear Modeling for Speech Processing includes advanced topics in non-linear estimation and modeling techniques along with their applications to speaker recognition.

Non-linear aeroacoustic modeling approach is used to estimate the important fine-structure speech events, which are not revealed by the short time Fourier transform (STFT). This aeroacostic modeling approach provides the impetus for the high resolution Teager energy operator (TEO). This operator is characterized by a time resolution that can track rapid signal energy changes within a glottal cycle.

The cepstral features like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the magnitude spectrum of the speech frame and the phase spectra is neglected. To overcome the problem of neglecting the phase spectra, the speech production system can be represented as an amplitude modulation-frequency modulation (AM-FM) model. To demodulate the speech signal, to estimation the amplitude envelope and instantaneous frequency components, the energy separation algorithm (ESA) and the Hilbert transform demodulation (HTD) algorithm are discussed.

Different features derived using above non-linear modeling techniques are used to develop a speaker identification system. Finally, it is shown that, the fusion of speech production and speech perception mechanisms can lead to a robust feature set.

Keywords

AM-FM model Aeroacoustic modeling approach Glottal cycle Non-linear modeling techniques Speech production mechanism Speech signal processing Teager energy operator

Authors and affiliations

  1. 1., Department of InstrumentationSGGS Institute of Engineering & TechnoloVishnupuri, NandedIndia
  2. 2., Department of E&TC EngineeringSRES College of EngineeringKopargaonIndia

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