Application of Zero-Frequency Filtering for Vowel Onset Point Detection

  • Anil Kumar Vuppala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)

Abstract

Vowel onset points in speech signals, are the instances where the voicing of the vowels begin. These points serve as important landmarks for the analysis as well as synthesis of speech signals. These landmarks help to identify the information about the behaviour of transition of several different sounds into and out of the vowel regions. In this paper, we propose a new method to identify vowel onset points for a speech signal using the zero frequency filtered (ZFF) speech signal and its frequency spectrum. The ZFF signal is obtained by passing the speech signal through a resonator with central frequency as 0 Hz. Therefore, ZFF signal essentially contains the low pass components of a given speech signal. Vowels are mostly characterized by the significant energy content in the relatively low frequency bands. Significant improvement in VOP detection performance is observed using proposed method compared to existing methods.

Keywords

Vowel onset point (VOP) Vowels zero frequency filtering frequency spectrum 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rao, K.S., Vuppala, A.K.: Non-uniform time scale modification using instants of significant excitation and vowel onset points. Elsevier Speech Communication 55(6), 745–756 (2013)CrossRefGoogle Scholar
  2. 2.
    Prasanna, S.R.M., Reddy, B.V.S., Krishnamoorthy, P.: Vowel onset point detection using source, spectral peaks, and modulation spectrum energies. IEEE Trans. on Audio, Speech, and Language Processing 17(4), 556–565 (2009)CrossRefGoogle Scholar
  3. 3.
    Prasanna, S.R.M., Gangashetty, S.V., Yegnanarayana, B.: Significance of vowel onset point for speech analysis. In: Proc. of Int. Conf. Signal Processing and Communications, Bangalore, India, pp. 81–88 (2001)Google Scholar
  4. 4.
    Vuppala, A.K., Rao, K.S., Chakrabarti, S.: Improved consonant-vowel recognition for low bit-rate coded speech. Wiley International Journal of Adaptive control and Signal processing 26(4), 333–349 (2012)CrossRefGoogle Scholar
  5. 5.
    Gangashetty, S.V., Sekhar, C.C., Yegnanarayana, B.: Detection of vowel onset points in continuous speech using autoassociative neural network models. In: Proc. Int. Conf. Spoken Language Processing, Jeju Island, Korea, pp. 401–410 (2004)Google Scholar
  6. 6.
    Vuppala, A.K., Rao, K.S., Chakrabarti, S.: Spotting and recognition of consonant-vowel units from continuous speech using accurate vowel onset points. Springer Circuits, Systems and Signal Processing 31(4), 1459–1474 (2012)CrossRefGoogle Scholar
  7. 7.
    Rao, K.S., Yegnanarayana, B.: Duration modification using glottal closure instants and vowel onset points. Speech Communication 51, 1263–1269 (2009)CrossRefGoogle Scholar
  8. 8.
    Vuppala, A.K., Rao, K.S.: Speaker identification under background noise using features extracted from steady vowel regions. Wiley International Journal of Adaptive control and Signal processing 29(9), 781–792 (2013)CrossRefGoogle Scholar
  9. 9.
    Vuppala, A.K., Yadav, J., Rao, K.S., Chakrabarti, S.: Vowel onset point detection for low bit rate coded speech. IEEE Transactions on Audio, Speech and Language Processing 20(6), 1894–1903 (2012)CrossRefGoogle Scholar
  10. 10.
    Hermes, D.J.: Vowel onset detection. J. Acoust. Soc. Amer. 87, 866–873 (1990)CrossRefGoogle Scholar
  11. 11.
    Wang, J.-F., Wu, C.H., Chang, S.H., Lee, J.Y.: A hierarchical neural network based C/V segmentation algorithm for Mandarin speech recognition. IEEE Trans. on Signal Processing 39(9), 2141–2146 (1991)CrossRefGoogle Scholar
  12. 12.
    Wang, J.-H., Chen, S.-H.: A C/V segmentation algorithm for Mandarin speech using wavelet transforms. In: Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Phoenix, Arizona, pp. 1261–1264 (1999)Google Scholar
  13. 13.
    Gangashetty, S.V., Sekhar, C.C., Yegnanarayana, B.: Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances. In: Proc. of IEEE ICISIP, pp. 159–164 (2004)Google Scholar
  14. 14.
    Prasanna, S.R.M., Yegnanarayana, B.: Detection of vowel onset point events using excitation source information. In: Proc. of Interspeech, Lisbon, Portugal, pp. 1133–1136 (2005)Google Scholar
  15. 15.
    Murty, K.S.R., Yegnanarayana, B.: Epoch extraction from speech signals. IEEE Trans. on Audio, Speech, and Language Processing 16(8), 1602–1613 (2008)CrossRefGoogle Scholar
  16. 16.
    Garofolo, J.S., Lamel, L.F., Fisher, W.M., Fiscus, J.G., Pallett, D.S., Dahlgren, N.L., Zue, V.: TIMIT acoustic-phonetic continuous speech corpus linguistic data consortium. In: Proc. of IEEE ICISIP, Philadelphia, PA (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Anil Kumar Vuppala
    • 1
  1. 1.Language Technologies Research CentreInternational Institute of Information TechnologyHyderabadIndia

Personalised recommendations