Characterization of Consonant Sounds Using Features Related to Place of Articulation

  • Pravin Bhaskar RamtekeEmail author
  • Srishti HegdeEmail author
  • Shashidhar G. Koolagudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)


Speech sounds are classified into 5 classes, grouped based on place and manner of articulation: velar, palatal, retroflex, dental and labial. In this paper, an attempt has been made to explore the role of place of articulation and vocal tract length in characterizing the different class of speech sounds. Formants and vocal tract length available for the production of each class of sound are extracted from the region of transition from consonant burst to the rising profile of the immediate following vowel. These features along with their statistical variations are considered for the analysis. Based on the non-linear nature of the features Random Forest (RF) is used for the classification. From the results, it is observed that the proposed features are efficient in discriminating the class of consonants: velar and palatal, palatal and retroflex and palatal and labial sounds with an accuracy of 92.9%, 93.83 and 94.07 respectively.


Formants Manner of articulation Place of articulation Random forest Vocal tract length 


  1. 1.
    Jones, D.: The phoneme: its nature and Use. Cambridge, England, Heffer (1950)Google Scholar
  2. 2.
    Denes, P.B.: On the statistics of spoken English. J. Acoust. Soc. Am. 35(6), 892–904 (1963)CrossRefGoogle Scholar
  3. 3.
    Clements, G.N.: Place of articulation in consonants and vowels: a unified theory. Work. Pap. Cornell Phon. Lab. 5, 77–123 (1991)Google Scholar
  4. 4.
    Rabiner, L. R., Juang, B. H.: Fundamentals of speech recognition. Tsinghua University Press (1999)Google Scholar
  5. 5.
    Hogg, R.M.: Phonology and morphology. Camb. Hist. Engl. Lang. 1, 67–167 (1992)CrossRefGoogle Scholar
  6. 6.
    Grunwell, P.: Phonological Assessment of Child Speech (PACS). College Hill Press (1985)Google Scholar
  7. 7.
    Shriberg, L.D., Kwiatkowski, J.: Phonological disorders I: A diagnostic classification system. J. Speech Hear. Disord. 47(3), 226–241 (1982)CrossRefGoogle Scholar
  8. 8.
    Eskenazi, M.: Using automatic speech processing for foreign language pronunciation tutoring: Some issues and a prototype. Lang. Learn. Tech. 2(2), 62–76 (1999)Google Scholar
  9. 9.
    Fukada, T., Tokuda, K., Kobayashi, T., Imai, S.: An adaptive algorithm for mel-cepstral analysis of speech. In: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992. ICASSP-92, vol. 1, pp. 137–140. IEEE (1992)Google Scholar
  10. 10.
    Shrawankar, U., Thakare, V.M.: Techniques for feature extraction in speech recognition system: A comparative study arXiv:1305.1145 (2013)
  11. 11.
    Zue, V.W.: The use of speech knowledge in automatic speech recognition. Proc. IEEE 73(11), 1602–1615 (1985)CrossRefGoogle Scholar
  12. 12.
    Milone, D.H., Rubio, A.J.: Prosodic and accentual information for automatic speech recognition. IEEE Trans. Speech Audio Process. 11(4), 321–333 (2003)CrossRefGoogle Scholar
  13. 13.
    Manjunath, K.E., Sreenivasa Rao, K.: Articulatory and excitation source features for speech recognition in read, extempore and conversation modes. Int. J. Speech Technol. 19(1), 121–134 (2016)CrossRefGoogle Scholar
  14. 14.
    Garofolo, J.S., Lamel, L.F., Fisher, W.M., Fiscus, J.G., Pallett, D.S.: Darpa timit acoustic-phonetic continous speech corpus. NASA STI/Recon technical report n 93, (1993)Google Scholar
  15. 15.
    McCandless, S.: An algorithm for automatic formant extraction using linear prediction spectra. IEEE Trans. Acoust., Speech, Signal Process. 22(2), 135–141 (1974)CrossRefGoogle Scholar
  16. 16.
    Stevens, K.N.: Acoustic Phonetics, vol. 30. MIT Press, Cambridge (2000)Google Scholar
  17. 17.
    Paige, A., Zue, V.: Calculation of vocal tract length. IEEE Trans. Audio Electroacoust. 18(3), 268–270 (1970)CrossRefGoogle Scholar
  18. 18.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology KarnatakaSurathkalIndia
  2. 2.Nitte Mahalinga Adyanthaya Memorial Institute of TechnologyKarkalaIndia

Personalised recommendations