cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sadrina
Contact Email
sadrina@ar-raniry.ac.id
Phone
-
Journal Mail Official
-
Editorial Address
Syeikh Abul Rauf Kopelma Darussalam Banda Aceh, Indonesia, Postal Code 23111
Location
Kota banda aceh,
Aceh
INDONESIA
CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro
Published by Universitas Islam Negeri Ar-Raniry Banda Aceh
ISSN : 25493698     EISSN : 25493701     DOI : 10.22373
Core Subject : Education, Engineering,
Education Electrical & Electronics Engineering Other
Journal Circuit is an Electrical Engineering Education Scientific journal which published by the Electrical Engineering Education Department, Faculty of Teaching and Training, Ar-Raniry State Islamic University, Banda Aceh. The Circuit Journal publishes empirical and theoretical contributions in the electrical engineering education scientific from the students, lecturers, professors or other scientists. The Circuit Journal manuscripts provide an original fundamental research, related to electrical engineering, electrical engineering education, and vocational education. The Circuit Journal also embossing the interpretative reviews, and discussion of new development in electrical engineering education
Arjuna Subject : -
Articles 11 Documents
 1   2 
Search results for , issue "Vol. 9 No. 2 (2025)" : 11 Documents clear
Optimizing Voiceprint Modelling for Biometric Authentication and Security: Applications in Public Safety and Surveillance Kikmo, Christophe Wilba; Philippe, Totto Ndong Mathias; Samuel, Batambock; Jean, Nyatte Nyatte; Andre, Abanda
Circuit: Jurnal Ilmiah Pendidikan Teknik Elektro Vol. 9 No. 2 (2025)
Publisher : PTE FTK UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/2e6p4114

Abstract

A novel biometric authentication framework based on voice recognition has recently gained prominence for applications in public security. This system employs a hybrid Deep Neural Network–Hidden Markov Model (DNN-HMM) architecture, optimized through the effective extraction of acoustic features using Mel-Frequency Cepstral Coefficients (MFCC). The distinctive innovation of this model lies in its ability to sustain an accuracy rate exceeding 95%, even under conditions of environmental noise and high intra-speaker variability. The system leverages a supervised learning framework that integrates the temporal modeling strengths of hidden Markov models with the discriminative capabilities of deep neural networks, thereby enabling real-time processing. Experimental results show that the system effectively resists threats like voice cloning and deepfake attacks, while also accelerating authentication procedures to meet strict cybersecurity standards. The model strictly adheres to confidentiality and informed consent requirements for voice data. Recent efforts to enhance algorithmic fairness have focused on mitigating linguistic biases related to diverse accents and dialects through comprehensive exploratory analyses. Future directions include integrating the system with multimodal biometric frameworks and expanding deployment via cloud-based infrastructures to ensure scalability. This advancement marks a significant step in intelligent voice authentication, harmonizing technological innovation with ethical accountability and robust security principles

Page 2 of 2 | Total Record : 11

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 9 No. 2 (2025) Vol 9, No 1 (2025) Vol. 9 No. 1 (2025) Vol 8, No 2 (2024) Vol. 8 No. 2 (2024) Vol. 8 No. 1 (2024) Vol 8, No 1 (2024) Vol 7, No 2 (2023) Vol. 7 No. 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 2 (2018): Edisi Agustus 2018 Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 2, No 1 (2018) Vol 1, No 2 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) Vol 2, No 1 (2016) Vol 1, No 2 (2015) Vol 1, No 1 (2015) More Issue