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Significance of Frequency Band Selection of MFCC for Text-Independent Speaker Identification

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Book cover Proceedings of the International Conference on Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 469))

Abstract

This paper presents significance of Mel-frequency Cepstral Coefficients (MFCC) Frequency band selection for text-independent speaker identification. Recent studies have been focused on speaker specific information that may extends beyond telephonic passband. The selection of the frequency band is an important factor to effectively capture the speaker specific information present in the speech signal for speaker recognition. This paper focuses on development of a speaker identification system based on MFCC features which are modeled using vector quantization. Here, the frequency band is varied up to 7.75 kHz. Speaker identification experiments evaluated on TIMIT database consisting of 630 speaker shows that the average recognition rate achieved is 97.37 % in frequency band 0–4.85 kHz for 20 MFCC filters.

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References

  1. Frédéric Bimbot, Jean-François Bonastre, Corinne Fredouille, Guillaume Gravier, Ivan Magrin-Chagnolleau, Sylvain Meignier, Teva Merlin, Javier Ortega-García, Dijana Petrovska-Delacrétaz, Douglas A. Reynolds: A tutorial on text-independent speaker verification, EURASIP Journal on Applied Signal Processing 2004, Hindawi, pp. 430–451 (2004).

    Google Scholar 

  2. Md Jahangir Alam, Tomi Kinnunen, Patrick Kenny, Pierre Ouellet, Douglas O’Shaughnessy: Multitaper MFCC and PLP features for speaker verification using i-vectors, Journal on Speech Communication, Elsevier, vol. 55, no. 2, pp. 237–251 (2013).

    Google Scholar 

  3. Claude Turner, Anthony Joseph, Murat Aksu, Heather Langdond: The Wavelet and Fourier Transforms in Feature Extraction for Text-Dependent, Filterbank-Based Speaker Recognition, Journal onProcedia Computer Science, Elsevier, vol. 6, pp. 124–129 (2011).

    Google Scholar 

  4. Mangesh S. Deshpande, Raghunath S. Holambe: New Filter Structure based Admissible Wavelet Packet Transform for Text-Independent Speaker Identification, International Journal of Recent Trends in Engineering, vol. 2, no. 5, pp. 121–125 (2009).

    Google Scholar 

  5. Dr. Shaila D. Apte: Speech Processing Applications, in Speech and Audio Processing, Section 1, Section 2 and Section 3, pp. 1–6, 67, 91–92, 105–107, 129–132, Wiley India Edition.

    Google Scholar 

  6. Tomi Kinnunen, Haizhou Li: An overview of text-independent speaker recognition: From features to supervectors, Journal onSpeech Communication, Elsevier, vol. 52, no. 1, pp. 12–40 (2010).

    Google Scholar 

  7. Tomi Kinnunen, Rahim Saeidi, FilipSedlák, Kong Aik Lee, Johan Sandberg, Maria Hansson-Sandsten, Haizhou Li: Low-Variance Multitaper MFCC Features: A Case Study in Robust Speaker Verification, IEEE Transactions Audio, Speech and Language Processing, vol.20, no.7, pp. 1990–2001 (2012).

    Google Scholar 

  8. Pawan K. Ajmera, Dattatray V. Jadhav, Ragunath S. Holambe: Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram, Journal on Pattern Recognition, Elsevier, vol. 44, no. 10–11, pp. 2749–2759 (2011).

    Google Scholar 

  9. WU Zunjing, CAO Zhigang: Improved MFCC-Based Feature for Robust Speaker Identification, TUP Journals & Magazines, vol.10, no 2, pp. 158–161 (2005).

    Google Scholar 

  10. Jian-Da Wu, Bing-Fu Lin: Speaker identification using discrete wavelet packet transform technique with irregular decomposition, Journal on Expert Systems with Applications, Elsevier, vol. 36, no. 2, pp. 3136–3143 (2009).

    Google Scholar 

  11. R. Shantha Selva Kumari, S. Selva Nidhyananthan, Anand.G: Fused Mel Feature sets based Text-Independent Speaker Identification using Gaussian Mixture Model, International Conference on Communication Technology and System Design 2011, Journal on Procedia Engineering, Elsevier, vol. 30, pp. 319–326 (2012).

    Google Scholar 

  12. Seiichi Nakagawa, Longbiao Wang, and Shinji Ohtsuka: Speaker Identification and Verification by Combining MFCC and Phase Information, IEEE Transactions Audio, Speech and Language Processing, vol.20, no.4, pp. 1085–1095 (2012).

    Google Scholar 

  13. Sumithra Manimegalai Govindan, Prakash Duraisamy, Xiaohui Yuan: Adaptive wavelet shrinkage for noise robust speaker recognition, Journal on Digital Signal Processing, Elsevier, vol. 33, pp. 180–190 (2014).

    Google Scholar 

  14. Noor Almaadeed, Amar Aggoun, Abbes Amira: Speaker identification using multimodal neural networks and wavelet analysis, IET Journals and Magazines, vol. 4, no. 1, pp. 18–28 (2015).

    Google Scholar 

  15. Khaled Daqrouq, Tarek A. Tutunji: Speaker identification using vowels features through a combinedmethod of formants, wavelets, and neural network classifiers, Journal on Applied Soft Computing, Elsevier, vol. 27, pp. 231–239 (2015).

    Google Scholar 

  16. Pradhan, G.; Prasanna, S.: Significance of speaker information in wideband speech, in Communications (NCC), 2011 National Conference on, pp. 1–5, (2011).

    Google Scholar 

  17. J.S. Garofolo, L.F. Lamel, W.M. Fisher, J.G. Fiscus, D.S. Pallett, N.L. Dahlgren, V. Zue, TIMIT acoustic-phonetic continuous speech corpus, http://catalog.ldc.upenn.edu/ldc93s1, 1993.

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Authors and Affiliations

  1. Department of Electronics Engineering, All India Shri Shivaji Memorial Society’s Institute of Information Technology, Pune, 411001, India

    S. B. Dhonde

  2. Department of Electronics and Telecommunication Engineering, TPCT COE, Osmanabad, 413501, India

    S. M. Jagade

Authors
  1. S. B. Dhonde
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  2. S. M. Jagade
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Correspondence to S. B. Dhonde .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Sabar Institute of Technology, Tajpur, Sabarkantha, Gujarat, India

    Amit Joshi

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Dhonde, S.B., Jagade, S.M. (2017). Significance of Frequency Band Selection of MFCC for Text-Independent Speaker Identification. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_21

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  • DOI: https://doi.org/10.1007/978-981-10-1678-3_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1677-6

  • Online ISBN: 978-981-10-1678-3

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