Text dependent voice recognition system using MFCC and VQ for security applications

Kumar, Ashwin Nair Anil; Muthukumaraswamy, Senthil Arumugam

doi:10.1109/iceca.2017.8212779

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…P. Korshunov et al [2] considered the fact that most biometric technology systems are vulnerable to spoofing which reduces their wide use sometimes hence they presented the need to develop anti-spoofing detection methods also referred to as presentation attack detection (PAD) systems. Arun Kumar Chaudhary et al [3] proposed their method which is an MFCC-centered speaker verification in a noisy atmosphere using VQ. The research evaluation was conducted on the speech signal in a daily noisy environment.…”

Section: Literature Workmentioning

confidence: 99%

Password Authentication System using Voice Recognition

2023

IRJMETS

View full text Add to dashboard Cite

In this digital world, ensuring the security of sensitive information and personal data is of paramount importance. Traditional text-based password systems are susceptible to various vulnerabilities, such as password sharing, hacking, and social engineering attacks. To enhance security and user experience innovative methods of authentication have emerged. One of which is voice recognition technology. The "Password Authentication System using Voice Recognition" is a cutting-edge authentication solution that leverages the unique characteristics of an individual's voice to grant or deny access to personal information. This study provides an overview of the password authentication system using voice recognition highlighting its key features, benefits, and potential applications.

show abstract

Section: Literature Workmentioning

confidence: 99%

Password Authentication System using Voice Recognition

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

“…where 𝑌 𝑡 is the target vector that constructs the adaptation criterion of the operator 𝐻 𝑎 to 𝑅 ̂𝑠𝑑 . The target vector 𝑌 𝑡 is calculated as (6):…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In order to identify the speaker's voice several methods are used to extract the special features of each voice, among them mel-frequency cepstral coefficients. Although numerous researchers chose it as their feature extraction method because of its several advantages [5], [6], it reaches its limit in the improvement of automatic speaker recognition system as described by references [7]- [9]. It needs a large voice training dataset and a long execution time to identify the voice of each speaker [10] and the same goes for other approaches such as principal component analysis (PCA), discrete wavelet transform (DWT) and empirical modal decomposition (EMD) as revealed by reference [11].…”

Section: Introductionmentioning

confidence: 99%

Comparative study to realize an automatic speaker recognition system

Abakarim

Abenaou

2022

IJECE

View full text Add to dashboard Cite

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.

show abstract

“…Based on log-likelihood logic decision, the identity of that speaker is accepted if the match is above a threshold. Once the verification result is accepted, the attendance in database for that speaker will be updated [13]…”

Section: Voice Recognitionmentioning

confidence: 99%

Development of Academic Attendance System Using Voice Verification

Lazam

Saparon

2021

MJCS

View full text Add to dashboard Cite

Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.

show abstract

Text dependent voice recognition system using MFCC and VQ for security applications

Cited by 6 publications

References 7 publications

Password Authentication System using Voice Recognition

Password Authentication System using Voice Recognition

Comparative study to realize an automatic speaker recognition system

Development of Academic Attendance System Using Voice Verification

Contact Info

Product

Resources

About