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Biometric Voice Recognition system using MFCC and GMM with EM

by Rahul Pudurkar, Shruti Patil, Gazala Ansari, Shaikh Phiroj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 26
Year of Publication: 2022
Authors: Rahul Pudurkar, Shruti Patil, Gazala Ansari, Shaikh Phiroj
10.5120/ijca2022922315

Rahul Pudurkar, Shruti Patil, Gazala Ansari, Shaikh Phiroj . Biometric Voice Recognition system using MFCC and GMM with EM. International Journal of Computer Applications. 184, 26 ( Aug 2022), 5-10. DOI=10.5120/ijca2022922315

@article{ 10.5120/ijca2022922315,
author = { Rahul Pudurkar, Shruti Patil, Gazala Ansari, Shaikh Phiroj },
title = { Biometric Voice Recognition system using MFCC and GMM with EM },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2022 },
volume = { 184 },
number = { 26 },
month = { Aug },
year = { 2022 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number26/32474-2022922315/ },
doi = { 10.5120/ijca2022922315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:27.048863+05:30
%A Rahul Pudurkar
%A Shruti Patil
%A Gazala Ansari
%A Shaikh Phiroj
%T Biometric Voice Recognition system using MFCC and GMM with EM
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 26
%P 5-10
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The current solutions for passwords that are used for authenticating users can be insecure sometimes and might be hacked easily. Securing data and confidential information is very important in today’s world. Biometric is known as a unique biological characteristic of a human being. Using it as a means for securing devices or certain data, has proved to be very useful in recent times. This paper aims to implement a voice-based login authentication system. The voice biometrics (voiceprint) of the user is taken as an input to help authenticate the individual, along with traditional password pin validation, resulting in two-factor authentication. The system tries to identify not only the features of the voice but also collect the signature words of the user (the words that the user speaks differently, e.g., dialects, pronunciation, etc.) and store them in the database. So, when the user is trying to login into the system, a random sentence will be displayed on the screen with their signature words present along with some random words. When the user speaks the sentence, the real-time voiceprint will be compared with the voiceprint present in the database stored during registration. If both these voiceprints match, the individual will be considered an authentic user and will be given permission to access the system. To avoid any kind of malpractice, random words help the system be more secure for taking real-time voiceprints.

References
  1. N. D. Londhe, M. K. Ahirwal and P. Lodha, "Machine learning paradigms for speech recognition of an Indian dialect," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, 2016, pp. 0780-0786, doi: 10.1109/ICCSP.2016.7754251.
  2. S. Singh and M. Yamini, "Voice based login authentication for Linux," 2013 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, 2013, pp. 619- 624, doi: 10.1109/ICRTIT.2013. 6844272.xs.
  3. Annie Shoup, Tanya Talkar, Jodie Chen, Anubhav Jain ashoup, tjtalkar, jodiec, ajain94,” An Overview and Analysis of Voice Authentication Methods”.
  4. N. H. Tandel, H. B. Prajapati and V. K. Dabhi, "Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 459-465, doi: 10.1109/ICACCS48705.2020.9074184.
  5. M. M. Reda, N. G. Mohammed and R. A. Abdel Azeem Abul Seoud, "SVBiComm: Sign-Voice Bidirectional Communication System for Normal, “Deaf/Dumb” and Blind People based on Machine Learning," 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, 2018, pp. 1-8, doi: 10.1109/CAIS.2018.8441985.
  6. Tahira Mahboob, Memoona Khanum, Malik Sikandar Hayat Khiyal Ruqia Bibi. “Speaker Identification Using GMM with MFCC” IJCSI International Journal of Computer Science Issues, Volume 12, Issue 2, March 2015.
  7. “A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering” (2018) Mr. Ajinkya N. Jadhav and Mr. Nagaraj V. Dharwadkar.
  8. R.R. Lawrence, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, In proc. Of IEEE, Vol. 77, No.2, 1989, pp. 257-286.
  9. Shaughnessy, “Interacting with computer by voice automatic speech recognition and synthesis”, In proc. Of IEEE, Vol. 9, No. 91, 2003, pp.1272-1305.
  10. K. Kolhatkar, M. Kolte, and J. Lele, “Implementation of pitch detection algorithms for pathological voices,” In2016 International Conference on Inventive Computation Technologies (ICICT) 2016Aug26 (Vol. 1, pp. 1-5). IEEE.
  11. “Text Independent Speaker Recognition System using GMM” (2012) S G Bagul and Prof R.K. Shastri.
Index Terms

Computer Science
Information Sciences

Keywords

Voice Recognition Signature Words GMM MFCC Voice Activity Detection (VAD).

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