Skip to main content
SpringerLink
Log in

Implicit excitation source features for robust language identification

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

In present work, the robustness of excitation source features has been analyzed for language identification (LID) task. The raw samples of linear prediction (LP) residual signal, its magnitude and phase components are processed at sub-segmental, segmental and supra-segmental levels for capturing the robust language-specific phonotactic information. Present LID study has been carried out on 27 Indian languages from Indian Institute of Technology Kharagpur-Multi Lingual Indian Language Speech Corpus (IITKGP-MLILSC). Gaussian mixture models are used to develop the LID systems using robust language-specific excitation source information. Robustness of excitation source information has been evinced in view of (i) background noise, (ii) varying amount of training data and (iii) varying length of test samples. Finally, the robustness of proposed excitation source features is compared with the well-known spectral features using LID performances obtained from IITKGP-MLILSC database. Segmental level excitation source features obtained from raw samples of LP residual signal and its phase component perform better at low SNR levels, compared with the vocal tract features.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • All India Radio. (AIR). Accessed Feb. 2014 from http://www.newsonair.nic.in/.

  • Atal, B. S. (1972). Automatic speaker recognition based on pitch contours. Journal of the Acoustical Society of America, 52(6), 1687–1697.

    Article  Google Scholar 

  • Bajpai, A., & Yegnanarayana, B. (2004). Exploring features for audio clip classification using LP residual and AANN models. In The international conference on intelligent sensing and information processing (ICISIP 2004), Chennai, India (pp. 305–310).

  • Bapineedu, G., Avinash, B., Gangashetty, S. V., & Yegnanarayana, B. (2009). Analysis of lombard speech using excitation source information. In INTERSPEECH-09, Brighton, UK, September 2009.

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological), 39(1), 1–38.

  • Foil, J. T. (1986). Language identification using noisy speech. In IEEE international conference on acoustics, speech, and signal processing (ICASSP) (pp. 861–864).

  • Goodman, F., Martin, A., & Wohlford, R. (1989). Improved automatic language identification in noisy speech. In International conference on acoustics, speech, and signal processing (ICASSP) (Vol. 1, pp. 528–531).

  • Gupta, C. S., Prasanna, S. R. M., & Yegnanarayana, B. (2002). Autoassociative neural network models for online speaker verification using source features from vowels. In IEEE international joint conference on neural networks (IJCNNS).

  • Kim, J. H., & Hong, K. S. (2006). Speech and gesture recognition-based robust language processing interface in noise environment. Intelligent Data Engineering and Automated Learning, 4224, 338–345.

    Google Scholar 

  • Li, M., & Narayanan, S. (2014). Simplified supervised I-vector modeling with application to robust and efficient language identification and speaker verification. Computer, Speech Language, 28, 940–958.

    Article  Google Scholar 

  • Lawson, A., McLaren, M., Lei, Y., Mitra, V., Scheffer, N., Ferrer, L., et al. (2013). Improving language identification robustness to highly channel degraded speech through multiple system fusion. In INTERSPEECH, Lyon, France, Aug. 2013.

  • Manchala, S., Prasad, V. K., & Janaki, V. (2014). GMM based language identification system using robust features. International Journal of Speech Technology, 17, 99–105.

    Article  Google Scholar 

  • Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings on IEEE, 63, 561–580.

    Article  Google Scholar 

  • Maity, S., Vuppala, A. K., Rao, K. S., & Nandi, D. (2012). IITKGP-MLILSC speech database for language identification. In National conference on communication (NCC).

  • Murty, K. S. R., & Yegnanarayana, B. (2006). Combining evidence from residual phase and MFCC features for speaker recognition. IEEE Signal Processing Letters, 13, 52–55.

    Article  Google Scholar 

  • Prasanna, S. R. M., Gupta, C. S., & Yegnanarayana, B. (2006). Extraction of speaker-specific excitation information from linear prediction residual of speech. Speech Communication, 48, 1243–1261.

    Article  Google Scholar 

  • Rao, K. S., & Koolagudi, S. G. (2013). Characterization and recognition of emotions from speech using excitation source information. International Journal of Speech Technology, 16, 181–201.

    Article  Google Scholar 

  • Reddy, V. R., Maity, S., & Rao, K. S. (2013). Identification of indian languages using multi-level spectral and prosodic features. International Journal of Speech Technology, 16(4), 489–511.

    Article  Google Scholar 

  • Rao, K. S., Maity, S., & Reddy, V. R. (2013). Pitch synchronous and glottal closure based speech analysis for language recognition. International Journal of Speech Technology, 16(4), 413–430.

    Article  Google Scholar 

  • Reynolds, D. A., & Rose, R. C. (1995). Robust text independent speaker identification using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Process, 3, 4–17.

    Article  Google Scholar 

  • Shahina, A., & Yegnanarayana, V. (2005). Language identification in noisy environments using throat microphone signals. In International conference on intelligent sensing and information processing.

  • Sreenivasa Rao, K., & Yegnanarayana, B. (2006). Prosody modification using instants of significant excitation. IEEE Transactions on Speech and Audio Processing, 14, 972–980.

    Article  Google Scholar 

  • Seshadri, G. P., & Yegnanarayana, B. (2009). Perceived loudness of speech based on the characteristics of glottal excitation source. The Journal of the Acoustical Society of America, 126, 2061–2071.

    Article  Google Scholar 

  • Sreenivasa Rao, K., Prasanna, S. R. M., & Yegnanarayana, B. (2007). Determination of instants of significant excitation in speech using hilbert envelope and group delay function. IEEE Signal Processing Letters, 14(10), 762–765.

  • Sreenivasa Rao, K., & Vuppala, A. K. (2013). Non-uniform time scale modification using instants of significant excitation and vowel onset points. Speech Communication, 55(6), 745–756.

  • Varga, A., & Steeneken, H. J. (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 Communication, 12, 247–251.

    Article  Google Scholar 

  • Vuppala, A. K., & Sreenivasa Rao, K. (2013). Speaker identification under background noise using features extracted from steady vowel regions. International Journal of Adaptive Control and Signal Processing, 27(9), 781–792.

  • Vuppala, A. K., & Sreenivasa Rao, K. (2013). Vowel onset point detection for noisy speech using spectral energy at formant frequencies. International Journal of Speech Technology, 16(2), 229–235.

  • Vuppala, A. K., Sreenivasa Rao, K., & Chakrabarti, S. (2012). Improved vowel onset point detection using epoch intervals. International Journal of Electronics and Communications, 66(8), 697–700.

  • Yegnanarayana, B., Avendano, C., Hermansky, H., & Murthy, P. S. (1997). Processing linear prediction residual for speech enhancement. In European conference on speech communication and technology (pp. 1399–1402).

  • Yegnanarayana, B., Prasanna, S., Zachariah, J., & Gupta, C. (2005). Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification system. IEEE Transactions on Speech and Audio Processing, 13, 575–582.

    Article  Google Scholar 

  • Yegnanarayana, B., Prasanna, S. R. M., & Sreenivasa Rao, K. (2002). Speech enhancement using excitation source information. In International conference on acoustics, speech, and signal processing (ICASSP).

  • Yegnanarayana, B., Swamy, R. K., & Murthy, K. S. R. (2009). Determining mixing parameters from multispeaker data using speech-specific information. IEEE Transactions on Audio, Speech, and Language Processing, 17(6), 1196–1207.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology, IIT Kharagpur, Kharagpur, 721302, West Bengal, India

    Dipanjan Nandi & K. Sreenivasa Rao

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Nagaland, Dimapur, 797103, Nagaland, India

    Debadatta Pati

Authors
  1. Dipanjan Nandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debadatta Pati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Sreenivasa Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Sreenivasa Rao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nandi, D., Pati, D. & Rao, K.S. Implicit excitation source features for robust language identification. Int J Speech Technol 18, 459–477 (2015). https://doi.org/10.1007/s10772-015-9288-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-015-9288-2

Keywords

Search

Navigation