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Spoken Language Identification Using Spectral Features

  • Shashidhar G. Koolagudi
  • Deepika Rastogi
  • K. Sreenivasa Rao
Part of the Communications in Computer and Information Science book series (CCIS, volume 306)

Introduction

Spoken Language Identification (SLI) is the process of identifying the language spoken by a speaker. Language identification has several applications in day-today life. It may be used in call centers (e.g., emergency and customer services), information directories (e.g., airport, hotel, and tourist attractions) dealing with speakers speaking different languages[1]. Humans perform language identification mainly based on the specific words(phonetic information) and pattern of pronunciation. Spectral features are known well to capture phonetic information from the speech utterances[2]. Therefore, in this work MFCC’s(Mel Frequency Cepstral Coefficients) are used. Language identification is mainly done using some pattern classifier namely GMM, SVM, ANN and HMM[3].

Keywords

Spectral Feature Gaussian Mixture Model Customer Service Call Center Tourist Attraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kumar, P., Biswas, A., Mishra, A.N., Chandra, M.: Spoken Language Identification Using Hybrid Feature Extraction Methods. Journal of Telecommunications 1(2) (March 2010)Google Scholar
  2. 2.
    Hieronymus, J.L., Kadambe, S.: Spoken Language Identification using large Vocabulary Speech Recognition. Bell Laboratories, 700 Mountain Avenue, Murray Hill, NJ 07974 Atlantic Aerospace Elect. Corp., 6404 Ivy Lane,Greenbelt, MD 20906Google Scholar
  3. 3.
    Savic, M., Acosta, E., Gupta, S.K.: An Automatic Language Identification System. In: International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 817–820 (1991)Google Scholar
  4. 4.
    Muthusamy, Y.K., Barnard, E., Cole, R.: Reviewing Automatic Language Identification. IEEE Signal Processing Magazine 11(4), 33–41 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shashidhar G. Koolagudi
    • 1
  • Deepika Rastogi
    • 1
  • K. Sreenivasa Rao
    • 2
  1. 1.School of ComputingGraphic Era UniversityDehradunIndia
  2. 2.Indian Institute of Technology KharagpurKharagpurIndia

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