Advertisement

International Journal of Speech Technology

, Volume 16, Issue 2, pp 229–235 | Cite as

Vowel onset point detection for noisy speech using spectral energy at formant frequencies

  • Anil Kumar Vuppala
  • K. Sreenivasa Rao
Article

Abstract

In this paper, we propose a method for robust detection of the vowel onset points (VOPs) from noisy speech. The proposed VOP detection method exploits the spectral energy at formant frequencies of the speech segments present in glottal closure region. In this work, formants are extracted by using group delay function, and glottal closure instants are extracted by using zero frequency filter based method. Performance of the proposed VOP detection method is compared with the existing method, which uses the combination of evidence from excitation source, spectral peaks energy and modulation spectrum. Speech data from TIMIT database and noise samples from NOISEX database are used for analyzing the performance of the VOP detection methods. Significant improvement in the performance of VOP detection is observed by using proposed method compared to existing method.

Keywords

Vowel onset point (VOP) Formant frequencies Glottal closure region Excitation source Spectral peaks Modulation spectrum 

References

  1. Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2004a). Detection of vowel onset points in continuous speech using autoassociative neural network models. In Proc. int. conf. spoken language processing (pp. 401–410). Google Scholar
  2. Gangashetty, S. V., Sekhar, C. C., & Yegnanarayana, B. (2004b). Extraction of fixed dimension patterns from varying duration segments of consonant-vowel utterances. In Proc. of IEEE ICISIP (pp. 159–164). Google Scholar
  3. Hermes, D. J. (1990). Vowel onset detection. The Journal of the Acoustical Society of America, 87, 866–873. CrossRefGoogle Scholar
  4. Joseph, M. A., Guruprasad, S., & Yegnanarayana, B. (2006). Extracting formants from short segments of speech using group delay functions. In Proc. of interspeech (pp. 1009–1012). Google Scholar
  5. Murty, K. S. R., & Yegnanarayana, B. (2008). Epoch extraction from speech signals. IEEE Transactions on Audio, Speech, and Language Processing, 16(8), 1602–1613. CrossRefGoogle Scholar
  6. Prasanna, S. R. M., & Yegnanarayana, B. (2005). Detection of vowel onset point events using excitation source information. In Proc. of interspeech (pp. 1133–1136). Google Scholar
  7. Prasanna, S. R. M., Gangashetty, S. V., & Yegnanarayana, B. (2001). Significance of vowel onset point for speech analysis. In Proc. of int. conf. signal processing and communications (pp. 81–88). Google Scholar
  8. Prasanna, S. R. M., Reddy, B. V. S., & Krishnamoorthy, P. (2009). Vowel onset point detection using source, spectral peaks, and modulation spectrum energies. IEEE Transactions on Audio, Speech, and Language Processing, 17(4), 556–565. CrossRefGoogle Scholar
  9. Rao, K. S., & Yegnanarayana, B. (2009). Duration modification using glottal closure instants and vowel onset points. Speech Communication, 51, 1263–1269. CrossRefGoogle Scholar
  10. Vuppala, A. K., Rao, K. S., Chakrabarti, S., Krishnamoorthy, P., & Prasanna, S. R. M. (2011). Recognition of consonant-vowel (cv) units under background noise using combined temporal and spectral preprocessing. International Journal of Speech Technology, 14(1). Google Scholar
  11. Vuppala, A. K., Rao, K. S., & Chakrabarti, S. (2012a). Improved consonant–vowel recognition for low bit-rate coded speech. Wiley International Journal of Adaptive Control and Signal Processing, 26(4), 333–349. CrossRefGoogle Scholar
  12. Vuppala, A. K., Rao, K. S., & Chakrabarti, S. (2012b). Spotting and recognition of consonant-vowel units from continuous speech using accurate vowel onset points. Circuits, Systems, and Signal Processing, 31(4), 1459–1474. CrossRefGoogle Scholar
  13. Wang, J.-H., & Chen, S.-H. (1999). A c/v segmentation algorithm for mandarin speech using wavelet transforms. In Proc. IEEE int. conf. acoust., speech, signal processing (pp. 1261–1264). Google Scholar
  14. Wang, J.-F., Wu, C. H., Chang, S. H., & Lee, J. Y. (1991). A hierarchical neural network based C/V segmentation algorithm for mandarin speech recognition. IEEE Transactions on Signal Processing, 39(9), 2141–2146. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.LTRCInternational Institute of Information Technology-HyderabadHyderabadIndia
  2. 2.School of Information TechnologyIndian Institute of TechnologyKharagpurIndia

Personalised recommendations