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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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Automatic Speaker Recognition Dependency on Both the Shape of Auditory Critical Bands and Speaker Discriminative MFCCs

JOKIC, I. See more information about JOKIC, I. on SCOPUS See more information about JOKIC, I. on IEEExplore See more information about JOKIC, I. on Web of Science, DELIC, V. See more information about  DELIC, V. on SCOPUS See more information about  DELIC, V. on SCOPUS See more information about DELIC, V. on Web of Science, JOKIC, S. See more information about  JOKIC, S. on SCOPUS See more information about  JOKIC, S. on SCOPUS See more information about JOKIC, S. on Web of Science, PERIC, Z. See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on Web of Science
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Author keywords
automatic speaker recognition, mel-frequency cepstral coefficients, energy correction, speaker discriminative, exponential auditory critical bands

References keywords
recognition(15), speech(12), speaker(10), processing(6), signal(5), mfcc(5), features(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 25 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04004
Web of Science Accession Number: 000368499800004
SCOPUS ID: 84949997146

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Accuracy of an automatic speaker recognition system predominantly depends on speaker models and features that are used. An influence of the shape of auditory critical bands and a contribution of individual components of MFCC-based feature vectors are investigated in the paper and some experimental results are presented and showed their impact on the accuracy of automatic speaker recognition. The speaker-discrimination capability of the MFCCs was experimentally determined by comparing training and test models for the same speaker. The experiments are conducted with three speech databases and showed that 0th and 19th (the last one) MFCCs are non speaker discriminative. The values of MFCCs are determined by the type of applied auditory critical band. The exponential auditory critical bands based on the lower part of exponential function have outperformed the speaker recognition accuracy of other auditory critical bands such as rectangular or triangular shape.

References | Cited By  «-- Click to see who has cited this paper

[1] F. de Leon, K. Martinez, "Enhancing timbre model using MFCC and its time derivatives for music similarity estimation," in Proc. 20th European Signal Processing Conference (EUSIPCO 2012), Bucharest, Romania, August 27 - 31, 2012, pp. 2005-2009.

[2] T. Kinnunen, H. Li, "An overview of text-independent speaker recognition: From features to supervectors," Speech Communication, vol. 52, no. 1, pp. 12-40, 2010.
[CrossRef] [Web of Science Times Cited 601]

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[4] J. P. Campbell, Jr., "Speaker recognition: a tutorial," Proceedings of the IEEE, Vol. 85, No. 9, pp. 1437-1462, 1997.
[CrossRef] [Web of Science Times Cited 667]

[5] M. M. Dobrovic, V. D. Delic, N. M. Jakovljevic, I. D. Jokic, "Comparison of the Automatic Speaker Recognition Performance over Standard Features," in Proc. of the 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics (SISY 2012), Subotica, Serbia, 20 - 22 September 2012, pp. 341 - 344.

[6] V. Tiwari, "MFCC and its applications in speaker recognition," International Journal on Emerging Technologies, vol. 1(1), pp. 19-22, 2010.

[7] C. Ittichaichareon, S. Suksri, and T. Yingthawornsuk, "Speech Recognition using MFCC," in Proc. International Conference on Computer Graphics, Simulation and Modeling (ICGSM'2012), July 28-29, 2012 Pattaya (Thailand), pp. 135-138.

[8] S. D. Dhingra, G. Nijhawan, P. Pandit, "Isolated speech recognition using MFCC and DTW," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August 2013, pp. 4085-4092.

[9] D. Neiberg, K. Elenius and K. Laskowski, "Emotion Recognition in Spontaneous Speech Using GMMs," in INTERSPEECH 2006 - ICSLP, 17-21 September 2006, Pittsburg, Pennsylvania, pp. 809-812.

[10] B. Panda, D. Padhi, K. Dash, Prof. S. Mohanty, "Use of SVM Classifier & MFCC in Speech Emotion Recognition System," International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March 2012, pp. 225-230.

[11] Y. Attabi, M. J. Alam, P. Dumouchel, P. Kenny, D. O'Shaughnessy, "Multiple windowed spectral features for emotion recognition," Published in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 26-31 May 2013, Vancouver, BC, pp. 7527-7531.

[12] D. Wu, B. Li, and H. Jiang, "Normalization and Transformation Techniques for Robust Speaker Recognition," Source: Speech Recognition, Technologies and Applications, Book edited by: France Mihelic and Janez Zibert, ISBN 987-953-7619-29-9, pp. 550, 311-330, November 2008, I-Tech, Vienna, Austria.

[13] I. Jokic, S. Jokic, Z. Peric, M. Gnjatovic, V. Delic, "Influence of the Number of Principal Components used to the Automatic Speaker Recognition Accuracy," Electronics and Electrical Engineering, Kaunas: Technologija, 2012, No. 7(123), pp. 83-86.

[14] B. Salna, J. Kamarauskas, "Evaluation of Effectiveness of Different Methods in Speaker Recognition," Electronics and Electrical Engineering, Kaunas: Technologija, 2010, No. 2(98), pp. 67-70.

[15] S. Molau, M. Pitz, R. Schl├╝ter, and H. Ney, "Computing Mel-Frequency Cepstral Coefficients on the Power Spectrum," in Proc. International Conference on Acoustic, Speech and Signal Processing, Salt Lake City, UT, June 2001, Vol. 1, pp. 73-76.

[16] C. Lee, D. Hyun, E. Choi, J. Go, and C. Lee, "Optimizing Feature Extraction for Speech Recognition," IEEE Transactions on Speech and Audio Processing, Vol. 11, No. 1, January 2003, pp. 80-87.
[CrossRef] [Web of Science Times Cited 16]

[17] R. F. Lyon, A. G. Katsiamis, E. M. Drakakis, "History and Future of Auditory Filter Models," Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS 2010), May 30 - June 2 2010, Paris, France, pp. 3809-3812.

[18] M. Siafarikas, T. Ganchev, N. Fakotakis, G. Kokkinakis, "Wavelet Packet Approximation of Critical Bands for Speaker Verification," International Journal of Speech Technology, ISSN 1381 - 2416, vol.10, no.4, 2007, Springer, pp. 197-218.

[19] A. C. den Brinker, "An interpretation of the auditory critical bands using a local Kautz transformation," in Proc. ProRISC 8th anual Workshop on Circuits, Systems and Signal Processing, Mierlo, The Netherlands, 27-28 Nov. 1997, pp. 83-88.

[20] B. R. Wildermoth, "Text-Independent Speaker Recognition Using Source Based Features," pp. 19-20, M. Phil. Thesis, Griffith University, Brisbane, Australia, January 2001.

[21] F. Cummins, M. Grimaldi, T. Leonard, and J. Simko, "The CHAINS speech corpus: CHaracterizing INdividual Speakers," in Proc. of SPECOM, 2006, pp. 1-6.

References Weight

Web of Science® Citations for all references: 1,583 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 72 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-04-22 11:20 in 71 seconds.

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Faculty of Electrical Engineering and Computer Science
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