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Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker’s Conditions

by Kamil Ismaila Adeniyi, Oyeyiola Abdulhamid K.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 11
Year of Publication: 2014
Authors: Kamil Ismaila Adeniyi, Oyeyiola Abdulhamid K.
10.5120/15767-4460

Kamil Ismaila Adeniyi, Oyeyiola Abdulhamid K. . Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker’s Conditions. International Journal of Computer Applications. 90, 11 ( March 2014), 38-42. DOI=10.5120/15767-4460

@article{ 10.5120/15767-4460,
author = { Kamil Ismaila Adeniyi, Oyeyiola Abdulhamid K. },
title = { Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker’s Conditions },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 11 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number11/15767-4460/ },
doi = { 10.5120/15767-4460 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:48.225584+05:30
%A Kamil Ismaila Adeniyi
%A Oyeyiola Abdulhamid K.
%T Comparative Study on the Performance of Mel-Frequency Cepstral Coefficients and Linear Prediction Cepstral Coefficients under different Speaker’s Conditions
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 11
%P 38-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper compares Mel-Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) features under three speaker conditions: waking up, being fully awake and being tired, to determine which is better at handling the effect of these variations. A Gaussian Mixture Model (GMM) Classifier was used for both features. Experimental results show an identification rate of 83. 3% in the MFCC based system when the speakers were just waking up, while the LPCC based system had a lower identification rate of 75%. Also, when the speakers were either fully awake or tired, the MFCC based system achieved an identification rate of 100%, while the LPCC based system had an Identification rate of 91. 7%. In speaker verification, under the first condition (Waking Up), there is a significant difference between the equal error rates (EER), 7. 9% for MFCC and 22. 0% for LPCC. Also, there is a significant difference between the total success rates (TSR) under this condition. 82. 5% for MFCC and 65. 0% for LPCC. Overall, MFCC achieved a better total success rate under the three conditions studied.

References
  1. Beigi, H. 2011, Fundamentals of Speaker Recognition, New York: Springer Science and Business Media, Inc.
  2. Rashid, R. A. , Mahalin, N. H. , Sarijari, M. A. , Abdul Aziz, A. A. , 2008. "Security system using biometric technology: Design and implementation of Voice Recognition System (VRS)," Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on, vol. , no. , pp. 898,902, 13-15.
  3. Currie, D. , 2003. Shedding some light on Voice Authentication, SANS Institute, GSEC- V1. 4b.
  4. http://en. wikipedia. org/wiki/Speaker_recognition
  5. http://www. nou. edu. ng/NOUN_OCL/pdf/SMS/MBF%20841%20EMERGING%20TECHNOLOGIES%20IN%20INFORMATION%20TECHNOLOGY. pdf
  6. Kinnunen, T. and Li, H. 2009. An Overview of Text-Independent Speaker Recognition: from Features to Supervectors, pp. 1-14.
  7. Reynolds, D. A. ; Rose, R. C. ; , 1995. "Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models", IEEE Trans. Speech, Audio Processing. vol. 3, no. 1, pp. 72-83.
  8. Abdallah, S. J. , Osman, I. M. and Mustafa, M. E. 2012. "Text-Independent Speaker Identification Using Hidden Markov Model", World of Computer Science and Information Technology Journal (WCSIT), Vol. 2, No. 6, pp 203-208.
  9. Ibiyemi T. S. , Aliyu S. A. , Akintola A. G. 2012. "Face and Speech Recognition Fusion in Personal Identification", International Journal of Computer Applications, vol. 47, no. 23, pp 36-41.
  10. Ouzounov, A. 2010. "Cepstral Features and Text-Dependent Speaker Identification – A Comparative Study", Cybernetics and Information Technologies, Vol. 10, No 1, pp 3-12.
  11. https://tv. unsw. edu. au/files//unswPDF/Chapter3. pdf
  12. www. ee. ucla. edu/~ingrid/ee213a/speech/vlad_present. pdf
  13. Teknomo, Kardi. K-Means Clustering Tutorials. http:\people. revoledu. comkardi tutorialkMean
  14. Ilyas, M. Z. , Samad, S. A. , Hussain, A. and Ishak, K. A. 2007. "Speaker Verification using Vector Quantization and Hidden Markov Model", Proc. The 5th Student Conference on Research and Development, Malaysia.
Index Terms

Computer Science
Information Sciences

Keywords

Mel-frequency cepstral coefficients linear prediction cepstral coefficients speaker recognition speaker's conditions.

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