Skip to main content

Modification of the Glottal Voice Characteristics Based on Changing the Maximum-Phase Speech Component

  • Conference paper
Analysis of Verbal and Nonverbal Communication and Enactment. The Processing Issues

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6800))

Abstract

Voice characteristics are influenced especially by the vocal cords and by the vocal tract. Characteristics known as voice type (normal, breathy, tense, falsetto etc.) are attributed to vocal cords. Emotion influences among others the tonus of muscles and thus influences also the vocal cords behavior. Previous research confirms a large dependence of emotional speech on the glottal flow characteristics. There are several possible ways for obtaining the glottal flow signal from speech. One of them is the decomposition of speech using the complex cepstrum into the maximum- and minimum-phase components. In this approach the maximum-phase component is considered as the open phase of the glottal flow signal. In this contribution we present experiments with the modification of the maximum-phase speech signal component with the aim to obtain synthetic emotional speech.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Iida, A., et al.: A corpus-based speech synthesis system with emotions. Speech Communication 40, 161–187 (2003)

    Article  MATH  Google Scholar 

  2. Vích, R.: Pitch Synchronous Linear Predictive Czech and Slovak Text-to-Speech Synthesis. In: Proc. of the 15th International Congress on Acoustics, ICA 1995, Trondheim, Norway, vol. III, pp. 181–184 (1995)

    Google Scholar 

  3. Vích, R.: Cepstral Speech Model, Padé Approximation, Excitation and Gain Matching in Cepstral Speech Synthesis. In: Jan, J. (ed.) BIOSIGNAL 2000, VUTIUM, Brno, pp. 77–82 (2000)

    Google Scholar 

  4. Gobl, C., Chasaide, A.N.: The role of voice quality in communicating emotion, mood and attitude. Speech Communication 40, 18–212 (2003)

    Article  MATH  Google Scholar 

  5. Airas, M., Alku, P.: Emotions in Vowel Segments of Continuous Speech: Analysis of the Glottal Flow Using the Normalized Amplitude Quotient. Phonetica 63, 26–46 (2006)

    Article  Google Scholar 

  6. Walker, J., Murphy, P.: A Review of Glottal Waveform Analysis. In: Stylianou, Y., Faundez-Zanuy, M., Esposito, A. (eds.) COST 277. LNCS, vol. 4391, pp. 1–21. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Bozkurt, B.: Zeros of the z-transform (ZZT) representation and chirp group delay processing for the analysis of source and filter characteristics of speech signals. Ph.D. Thesis, Faculté Polytechnique De Mons, Belgium (2005)

    Google Scholar 

  8. Drugman, T., Bozkurt, B., Dutoid, T.: Complex Cepstrum-based Decomposition of Speech for Glottal Source Estimation. In: INTERSPEECH 2009, Brighton, U.K, pp. 116–119 (2009)

    Google Scholar 

  9. Doval, B.: Alessandro, Ch., Henric, N.: The voice source as a causal/anticausal linear filter. In: Proc. of ISCA Tutorial and Research Workshop on Voice Quality (VOQUAL), Geneva, pp. 15–19 (2003)

    Google Scholar 

  10. Tribolet, J.: A new phase unwrapping algorithm. IEEE Transactions on Acoustics, Speech and Signal Processing 25(2), 170–177 (1977)

    Article  MATH  Google Scholar 

  11. Oppenheim, A.V., Schafer, R.V.: Discrete-Time Signal Processing, pp. 768–825. Prentice Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  12. Vích, R.: Nichtkausales Cepstrales Sprachmodell. In: Proc. 20th Electronic Speech Processing Conference – ESSV 2009, Dresden, Germany, pp. 107–114 (2009)

    Google Scholar 

  13. Vondra, M., Vích, R.: Speech Conversion Using a Mixed-phase Cepstral Vocoder. In: Proc. of 21st Electronic Speech Processing Conference – ESSV 2010, Berlin, Germany, pp. 112–118 (2010)

    Google Scholar 

  14. Stylianou, Y.: Decomposition of speech signals into a deterministic and a stochastic part. In: Proc. of Fourth International Conference on Spoken Language, ICSLP 1996, Philadelphia, pp. 1213–1216 (1996)

    Google Scholar 

  15. Doval, B., d’Alessandro, C., Henrich, N.: The spectrum of glottal flow models, http://rs2007.limsi.fr/index.php/PS:Page_2

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vondra, M., VĂ­ch, R. (2011). Modification of the Glottal Voice Characteristics Based on Changing the Maximum-Phase Speech Component. In: Esposito, A., Vinciarelli, A., Vicsi, K., Pelachaud, C., Nijholt, A. (eds) Analysis of Verbal and Nonverbal Communication and Enactment. The Processing Issues. Lecture Notes in Computer Science, vol 6800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25775-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25775-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25774-2

  • Online ISBN: 978-3-642-25775-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics