Skip to main content

Language Identification Based on the Variations in Intonation Using Multi-classifier Systems

  • 1188 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10682)


In this article we make use of the characteristics of tonal languages and machine learning methodologies to understand the patterns in them. Instead of analyzing the absolute pitch or frequency, we analyze how one tone transitions to another in speech. Features (namely, zero crossing count, short time energy, minimum formant frequency, maximum formant frequency) are extracted using the tonal transitions over segments of audio signals. We have developed a multi-classifier system using four classifiers, namely maximum likelihood estimate (MLE), minimum distance classifier (MDC), k-nearest neighbor (kNN) classifier and fuzzy k-NN classifier to automatically identify tonal languages from audio signals. Initially, each individual classifier is trained with existing known data represented by the extracted features. The trained classifier is then used for language identification. Results obtained from these classifiers are combined to generate the final output. Experiments are conducted using three different tonal languages, namely, Chinese, Thai and Vietnamese. The output reveals that the developed multi-classifier model is able to produce promising results. The extracted features produced better results in comparison to usually used frequency value (as a feature). Ensemble of classifiers is a better tool than using individual classifiers.


  • Tonal language
  • Language identification
  • Classification
  • Multi-classifier

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and 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

Learn about institutional subscriptions


  1. Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Pearson, New Delhi (2014)

    Google Scholar 

  2. Muthusamy, Y.K., Barnard, E., Cole, R.A.: Reviewing automatic language identification. IEEE Sign. Process. Mag. 11, 33–41 (1994)

    CrossRef  Google Scholar 

  3. Zissman, M.A.: Automatic language identification of telephone speech. Lincoln Laboratory Manual, MIT, USA, vol. 8, no. 2, pp. 115–144 (1995)

    Google Scholar 

  4. Ambikairajah, E., Li, H., Wang, L., Yin, B., Sethu, V.: Language identification: a tutorial. IEEE Circ. Syst. Mag. 11(2), 82–108 (2011)

    CrossRef  Google Scholar 

  5. Ng, R.W.M., Lee, T., Leung, C., Ma, B., Li, H.: Spoken language recognition with prosodic features. IEEE Trans. Audio Speech Lang. Process. 21(9), 1841–1852 (2013)

    CrossRef  Google Scholar 

  6. Itahashi, S., Zhou, J.X., Tanaka, K.: Spoken language discrimination using speech fundamental frequency. In: Proceedings of Third International Conference on Spoken Language Processing, Japan, vol. 4, pp. 1899–1902 (1994)

    Google Scholar 

  7. Tong, R., Ma, B., Zhu, D., Li, H., Chng, E.S.: Integrating acoustic, prosodic and phonotactic features for spoken language identification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. I 205–I 208 (2006)

    Google Scholar 

  8. Rao, K.S., Yegnanarayana, B.: Intonation modeling for Indian languages. J. Comput. Speech Lang. 23, 240–256 (2009)

    CrossRef  Google Scholar 

  9. Newman, J.L., Cox, S.J.: Language identification using visual features. IEEE Trans. Audio Speech Lang. Process. 20(7), 1936–1947 (2012)

    CrossRef  Google Scholar 

  10. Segbroeck, M., Travadi, R., Narayanan, S.S.: Rapid language identification. IEEE Trans. Audio Speech Lang. Process. 23(7), 1118–1129 (2015)

    CrossRef  Google Scholar 

  11. Yencken, L.: The great language game (2013).

  12. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  13. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier, New York (2008)

    MATH  Google Scholar 

  14. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Education, New Delhi (2009)

    Google Scholar 

  15. Cannam, C., Landone, C., Sandler, M.: Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1467–1468 (2010)

    Google Scholar 

Download references


An earlier version of this work has been presented at the Intel International Science and Engineering Fair (Intel ISEF), held at Los Angeles, USA in May 2017 and won a Grand Award. The author would like to acknowledge her School teacher, Dr. Partha Pratim Roy, for advising her throughout the course of this work. Thanks are due to the Intel Initiative for Research and Innovation in Science (IRIS) Scientific Review Committee and her mentors, for their valuable comments. The author also acknowledges Rahul Roy and Ajoy Mondal, her parents’ students, for helping her in conducting the experiments.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shinjini Ghosh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, S. (2017). Language Identification Based on the Variations in Intonation Using Multi-classifier Systems. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)