Advertisement

Automatic Text-Independent Kannada Dialect Identification System

  • Nagaratna B. ChittaragiEmail author
  • Asavari Limaye
  • N. T Chandana
  • B Annappa
  • Shashidhar G. Koolagudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

This paper proposes a dialect identification system for the Kannada language. A system that can automatically identify the dialects of the language being spoken has a wide variety of applications. However, not many Automatic Speech Recognition (ASR) and dialect identification tasks are carried out in majority of the Indian languages. Further, there are only a few good quality annotated audio datasets available. In this paper, a new dataset for 5 spoken dialects of the Kannada language is introduced. Spectral and prosodic features have captured the most prominent features for recognition of Kannada dialects. Support Vector Machine (SVM) and neural networks algorithms are used for modeling text-independent recognition system. A neural network model that attempts for identification dialects based on sentence level cues has also been built. Hyper-parameters for SVM and neural network models are chosen using grid search. Neural network models have outperformed SVMs when complete utterances are considered.

Keywords

Dialect identification Sentence level Complete utterance level MFCCs Support vector machines Neural networks 

References

  1. 1.
    Harris, M.J., Gries, S.T., Miglio, V.G.: Prosody and its application to forensic linguistics. LESLI: Linguist. Evid. Secur. Law Intell. 2(2), 11–29 (2014)CrossRefGoogle Scholar
  2. 2.
    Rouas, J.L.: Automatic prosodic variations modeling for language and dialect discrimination. IEEE Trans. Audio Speech Lang. Process. 15(6), 1904–1911 (2007)CrossRefGoogle Scholar
  3. 3.
    Huang, R., Hansen, J.H.L., Angkititrakul, P.: Dialect/accent classification using unrestricted audio. IEEE Trans. Audio Speech Lang. Process. 15(2), 453–464 (2007)CrossRefGoogle Scholar
  4. 4.
    Zissman, M.A.: Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. Speech Audio Process. 4(1), 31–44 (1996)CrossRefGoogle Scholar
  5. 5.
    Chittaragi, N.B., Koolagudi, S.G.: Acoustic features based word level dialect classification using SVM and ensemble methods. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–6 (2017)Google Scholar
  6. 6.
    Lei, Y., Hansen, J.H.L.: Dialect classification via text-independent training and testing for Arabic, Spanish, and Chinese. IEEE Trans. Audio Speech Lang. Process. 19(1), 85–96 (2011)CrossRefGoogle Scholar
  7. 7.
    Clopper, C.G., Smiljanic, R.: Effects of gender and regional dialect on prosodic patterns in American English. J. Phon. 39(2), 237–245 (2011)Google Scholar
  8. 8.
    Sinha, S., Jain, A., Agrawal, S.S.: Speech processing for Hindi dialect recognition. In: Advances in Signal Processing and Intelligent Recognition Systems, pp. 161–169 (2014)Google Scholar
  9. 9.
    Rao, K.S., Koolagudi, S.G.: Identification of Hindi dialects and emotions using spectral and prosodic features of speech. Int. J. Syst. Cybern. Inform. 9(4), 24–33 (2011)Google Scholar
  10. 10.
    Soorajkumar, R., Girish, G.N., Ramteke, P.B., Joshi, S.S., Koolagudi, S.G.: Text-independent automatic accent identification system for Kannada language. In: Proceedings of the International Conference on Data Engineering and Communication Technology, pp. 411–418. Springer, Berlin (2017)Google Scholar
  11. 11.
    Giannakopoulos, T., Pikrakis, A.: Introduction to Audio Analysis: A MATLAB Approach. Academic, Amsterdam (2014)Google Scholar
  12. 12.
    Chittaragi, N.B., Prakash, A., Koolagudi, S.G.: Dialect identification using spectral and prosodic features on single and ensemble classifiers. Arab. J. Sci. Eng. 43, 4289–4302 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Nagaratna B. Chittaragi
    • 1
    • 2
    Email author
  • Asavari Limaye
    • 1
  • N. T Chandana
    • 1
  • B Annappa
    • 1
  • Shashidhar G. Koolagudi
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkal, MangaloreIndia
  2. 2.Department of Information Science and EngineeringSiddaganga Institute of TechnologyTumkurIndia

Personalised recommendations