Modeling of the Speech Process Including Anatomical Structure of the Vocal Tract

  • Zygmunt Ciota
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 27)


The most important features of voice processing have been presented. The properties of glottal waves have been extracted using recorded microphone signals of the speech. Therefore, it was necessary to solve the inverse problem, of finding the glottis input of the whole vocal tract, having the resulting output waves of the speech process. The frequency parameters of glottal waves have been extracted using a vocal tract model. The autocorrelation and cepstrum methods are also helpful in such extraction. The results are important not only for speaker identification and emotion recognition, but can also be helpful for glottis malfunction diagnosis.


Speech Signal Emotion Recognition Vocal Tract Speaker Verification Speaker Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brookes M, Naylor PA, Gudnason J (2006) A quantitative assessment of group delay methods for identifying glottal closures in voiced speech. IEEE T Audio Speech Lang Processing 14:456–466CrossRefGoogle Scholar
  2. 2.
    Lee CM, Narayanan SS (2005) Toward detecting emotions in spoken dialogs. IEEE T Audio Speech Lang Processing, 13:293–303CrossRefGoogle Scholar
  3. 3.
    Ciota Z (2004) Speaker verification for multimedia application. In: Proceedings of the IEEE international conference on systems, man and cybernetics. The Hague, The Netherlands, pp 2752–2756Google Scholar
  4. 4.
    Ciota Z (2005) Emotion recognition on the basis of human speech. In: Proceedings of the international conference on applied electromagnetics and communications. Dubrovnik, Croatia, pp 467–470Google Scholar
  5. 5.
    Deng H, Ward RK, Beddoes MP, Hodgson M (2006) A new method for obtaining accurate estimates of vocal-tract filters and glottal waves from vowel sounds. IEEE T Audio Speech Lang Processing 14:445–455CrossRefGoogle Scholar
  6. 6.
    Gray P, Hollier MP, Massara RE (2000) Non-intrusive speech-quality assessment using vocal-tract models. IEE P-Vis Image Signal Processing 147:493–501CrossRefGoogle Scholar
  7. 7.
    Mozzafry B, Tinati MA, Aghagolzadeh A, Erfanian A (2006) An adaptive algorithm for speech source separation in overcomplete cases using wavelet packets. In: Proceedings of the 5th WSEAS international conference on signal processing. Istanbul, Turkey, pp 140–144Google Scholar
  8. 8.
    Santon J (ed) (1996) Progress in speech synthesis. Springer, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Zygmunt Ciota
    • 1
  1. 1.Department of Microelectronics and Computer ScienceTechnical University of LodzLodzPoland

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