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Language Identification Using Spectral Features

  • K. Sreenivasa RaoEmail author
  • V. Ramu Reddy
  • Sudhamay Maity
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series

Abstract

This chapter introduces multilingual Indian language speech corpus consisting of 27 regional Indian languages for analyzing the language identification (LID) performance. Speaker-dependent and independent language models are also discussed in view of LID. Spectral features extracted from conventional block processing, pitch synchronous analysis, and glottal closure regions are examined for discriminating the languages.

Keywords

Language identification Spectral features IITKGP-MLILSC OGI-MLTS Speaker dependent LID Speaker independent LID Speaker specific LID models Pitch synchronous spectral features Spectral features from glottal closure regions Zero frequency filter 

References

  1. 1.
    Rao KS, Maity S, Reddy VR (2013) Pitch synchronous and glottal closure based speech analysis for language recognition. Int J Speech Technol (Springer) 16(4):413–430Google Scholar
  2. 2.
    Maity S, Vuppala AK, Rao KS, Nandi D (2012) IITKGP-MLILSC speech database for language identification. In: National conference on communication, Feb 2012Google Scholar
  3. 3.
    Muthusamy YK, Cole RA, Oshika BT (1992) The OGI multi-language telephone speech corpus. In: Proceedings of international conference spoken language processing, pp 895–898, Oct 1992Google Scholar
  4. 4.
    Lander T, Cole R, Oshika B, Noel M (1995) The OGI 22 language telephone speech corpus. In: Proceedings of EUROSPEECH-1995, pp 817–820Google Scholar
  5. 5.
    Zheng F, Zhang G, Song Z (2001) Comparison of different implementations of MFCC. J Comput Sci Technol 16(6):582–589Google Scholar
  6. 6.
    Reynolds D (2009) Enclopedia of biometrics. Springer, New York, pp 659–663Google Scholar
  7. 7.
    Murty K, Yegnanarayana B (2008) Epoch extraction from speech signals. IEEEASLP 16:1602–1613Google Scholar
  8. 8.
    Sreenivasa Rao K, Prasanna SRM, Yegnanarayana B (2007) Determination of instants of significant excitation in speech using Hilbert envelope and group delay function. In: IEEE signal processing letters, vol 14, no 10, pp 762–765, Oct 2007Google Scholar
  9. 9.
    Varga A, Steeneken HJ (1993) Assessment for automatic speech recognition: II. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Commun 12:247–251CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • K. Sreenivasa Rao
    • 1
    Email author
  • V. Ramu Reddy
    • 2
  • Sudhamay Maity
    • 3
  1. 1.Indian Institute of Technology KharagpurKharagpurIndia
  2. 2.Innovation Lab KolkataKolkataIndia
  3. 3.Indian Institute of Technology KharagpurKharagpurIndia

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