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Spoken Language Identification Using Spectral Features

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Contemporary Computing (IC3 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 306))

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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].

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References

  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)

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  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 20906

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  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)

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  4. Muthusamy, Y.K., Barnard, E., Cole, R.: Reviewing Automatic Language Identification. IEEE Signal Processing Magazine 11(4), 33–41 (1994)

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Author information

Authors and Affiliations

  1. School of Computing, Graphic Era University, Dehradun, 248002, UK, India

    Shashidhar G. Koolagudi & Deepika Rastogi

  2. Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    K. Sreenivasa Rao

Authors
  1. Shashidhar G. Koolagudi
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  2. Deepika Rastogi
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  3. K. Sreenivasa Rao
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Editor information

Editors and Affiliations

  1. TASSL, Dept. of Electrical & Computer Engineering, Rutgers the State University of New Jersey, Brett Road, 08854-8058, Piscataway, NJ, USA

    Manish Parashar

  2. Mathematics and Computer Science Division, Argonne National Laboratory, 60439, Argonne, IL, USA

    Dinesh Kaushik

  3. School of Computer Science and Welsh Science Center, Cardiff University, 5 The Parade, CF24 3AA, Cardiff, UK

    Omer F. Rana

  4. Division of Physical Sciences and Engineering, 4700 King Abdullah University of Science and Technology, Room 3221, Al Jazri Building, 23955-6900, Thuwal, Makkah, Saudi Arabia

    Ravi Samtaney

  5. Department of Electrical and Computer Engineering, Stony Brook University, 11794, Stony Brook, New York, USA

    Yuanyuan Yang

  6. Faculty of Engineering and Information Technologies, School of Information Technologies, University of Sydney, 2006, Sydney, NSW, Australia

    Albert Zomaya

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© 2012 Springer-Verlag Berlin Heidelberg

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Koolagudi, S.G., Rastogi, D., Rao, K.S. (2012). Spoken Language Identification Using Spectral Features. In: Parashar, M., Kaushik, D., Rana, O.F., Samtaney, R., Yang, Y., Zomaya, A. (eds) Contemporary Computing. IC3 2012. Communications in Computer and Information Science, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32129-0_52

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  • DOI: https://doi.org/10.1007/978-3-642-32129-0_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32128-3

  • Online ISBN: 978-3-642-32129-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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