CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter

by Sahil Arora, Nirvair Neeru
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 9
Year of Publication: 2016
Authors: Sahil Arora, Nirvair Neeru
10.5120/ijca2016910854

Sahil Arora, Nirvair Neeru . Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter. International Journal of Computer Applications. 146, 9 ( Jul 2016), 17-24. DOI=10.5120/ijca2016910854

@article{ 10.5120/ijca2016910854,
author = { Sahil Arora, Nirvair Neeru },
title = { Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 9 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number9/25426-2016910854/ },
doi = { 10.5120/ijca2016910854 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:57.855863+05:30
%A Sahil Arora
%A Nirvair Neeru
%T Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 9
%P 17-24
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper deals with the identification of speakers identity from the given set of values of speech from the database. The major problem during the identification of speech is noisy environment which degrades the system performance during its mismatch. So one can say identification using speaker recognition is the vital issue in research. This paper tells about the various used techniques like GFCC i.e. Gamma tone Frequency Cepstral Coefficients as its speech detection algorithm and Gaussian Mixture Model (GMM) to estimate the Gaussian model parameters. This paper basically focuses on improvement of speech identification in noisy environment using Wavelet filter which are added to de-noise the speech signals. These techniques are applied on store value of databases in Attendance system application. and the features of the speech are then matched from the database. Experiment are done 15 speech values saying phrases ‘Present Mam’,’Present sir’,’Yes mam’,’Yes sir’with 4 types of utterance for each phase. This Experiment shows better results for stored database oriented system and gives 85% of the correct recognition rate i.e. CORR and 73% results are given when wavelet filter are not used .

References
  1. Nur Izzati Zainal, Khairul Azami Sidek, Teddy surya Gunawan, Hasmah Mansor, and Mire Kartiwi, “Design and development of portable classroom attendance system based on Arduino and fingerprint Biometric”, IEEE international conference on information and communication Technology, 2014.
  2. Engr. Imran Anwar Ujan and Dr. Imdad Ali Ismaili, “Biometric Attendance System”, IEEE International Conference on Complex Medical Engineering, 2011.
  3. Tsai-Cheng Li, Huan-Wen Wu, and Tiz-Shiang Wu1, “The study of Biometrics Technology Applied in Attendance Management System”, IEEE International Conference on Digital Manufacturing & Automation, pp. 943 – 947, 2012.
  4. Teh Wei Hsiung and Shahrizat Shaik Mohamed, “Performance of Iris Recognition using Low Resolution Iris Image for Attendance Monitoring”, IEEE International Conference on Computer Applications and Industrial Electronics, 2011.
  5. Mashhood Sajid, Rubab Hussain, and Muhammad Usman, “A Conceptual Model for Automated Attendance Marking System Using Facial Recognition”, IEEE International Conference on Digital Information Management, 2014.
  6. Subhadeep Dey, Sujit Barman, Ramesh K. Bhukya, Rohan K. Das, Haris B C, S. R. M. Prasanna, and R. Sinha, “Speech Biometric Based Attendance System”, IEEE National Conference on Communications, 2014.
  7. Aamir Nizam Ansari, Arundhati Navada, Sanchit Agarwal, Siddharth Patil, and Balwant A. Sonkamble, “Automation of Attendance System using RFID, Biometrics, GSM Modem with .Net Framework”, IEEE International Conference on Multimedia Technology, pp. 2976 – 2979, 2011.
  8. Balazs Benyo, Balint Sodor, Tibor Doktor, and Gergely Fordo, “Student attendance monitoring at the university using NFC”, IEEE, pp. 1 – 5, 2012.
  9. Zhao X., Shao Y., and Wang D.L., “CASA-based robust speaker identification”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, pp. 1608-1616, 2012.
  10. X Zhao, and DL Wang, “Analyzing noise robustness of MFCC and GFCC features in speaker identification”, IEEE International conference on acoustics, speech and signal processing, pp. 7204–7208, 2013.
  11. El Bachir TAZI, Abderrahim BENABBOU, Mostafa HARTl, "Efficient Text Independent Speaker Identification Based on GFCC and CMN Methods", IEEE International Conference on Multimedia Computing and Systems, pp. 90 – 95, 2012.
  12. Douglas A. Reynolds, and Richard C. Rose, “Robust text-independent speaker identification using Gaussian mixture speaker models”, IEEE Transaction Speech and Audio Processing, Vol. 3, pp 72–83, 1995.
  13. Liu Jiqing, Dong Yuan, Huang Jun, Zhao Xianyu, Wang Haila, "Sports audio classification based on MFCC and GMM", IEEE International Conference Broadband Network & Multimedia Technology, pp. 482 – 485, 2009.
  14. Md Jahangir Alam , Pierre Ouellet, Patrick Kenny, Douglas O’Shaughnessy, “Comparative Evaluation of Feature Normalization Techniques for Speaker Verification”, Nonlinear Speech Process., pp. 246–253, 2011
  15. Jelil S, Kachari G, and Joyprakash Singh, “Comparative evaluation of feature normalization techniques for voice password based speaker verification”, IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, pp. 1-4, 2013.
  16. Madiha Jalil, Faran Awais Butt, and Ahmed Malik, “Short-Time Energy, Magnitude, Zero Crossing Rate and Autocorrelation Measurement for Discriminating Voiced and Unvoiced segments of Speech Signals”, IEEE International Conference on Electronics and Computer Engineering, pp. 208 – 212, 2013.
  17. G. Saha, Sandipan Chakroborty, and Suman Senapati, “A New Silence Removal and Endpoint Detection Algorithm for Speech and Speaker Recognition Applications”, Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur, Kharagpur-721 302, India.
  18. Adrian E. Villanueva- Luna, Alberto Jaramillo-Nuñez,Daniel Sanchez-Lucero, Carlos M. Ortiz-Lima, J. Gabriel Aguilar-Soto, Aaron Flores-Gil and Manuel May-Alarcon, "De-Noising Audio SignalsUsing MATLAB Wavelets Toolbox", www.intechopen.com.
  19. Anil K. Jain, Arun Ross, and Salil Prabhakar, “An Introduction to Biometric Recognition”, IEEE Transactions on circuits and systems for video technology, vol. 14, 2004.
  20. Malcolm Slaney, "An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank", Advanced Technology Group, Apple Computer, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

Gammatone Frequency Cepstral Coefficients (GFCC) Gaussian Mixture Model (GMM) Cepstral mean normalization (CMN) Robust Speaker Identification Additive White Gaussian Noise (AWGN) Wavelet Filter End detection of input signal.

Powered by PhDFocusTM