VOICE MODULATION AND VERIFICATION FOR SMART AUTHENTICATION SYSTEM (original) (raw)

Text-Independent Voice Biometric for User Recognition

2021

This paper implements the biometric system's category of textindependent voice recognition based upon randomly implied text for user recognition and authentication in order to provide security to a user who is living alone at home. Feature extraction is one of the basic processes to start with any recognition technique. This work uses a combination of MFCC and RASTA-PLP (known as MFRASTA) to extract voice features and the Matlab tool is used for the implementation of this system along with 3G Dongle for sending the text messages on cell phones. An improvement of 98% is achieved in testing time over individual technique MFCC in terms of recognition rate.

Voice Biometric Systems for User Identification and Authentication – A Literature Review

International journal of applied engineering and management letters, 2022

Biometric trends are used in many systems because of security aspects. The cryptosystem is such an example which uses a biometric. But due to stored biometric data for the authentication, this can be a dangerous issue. Therefore, in comparison to conventional used biometric system, voice biometric system provides an efficient safety, security and unique identity. Among various speech recognition or processing methods, there is one called automated speech conversion methods, which also used to convert the recorded voice into text format. The overall concept of voice reorganization and voice biometric system is based on the acoustic modelling. Therefore, for getting the perfect speech detection, robust acoustic modelling is required. Our analysis describes the advancement and usage of voice biometric system for user identification and authentication. This paper provides a descriptive review of different voice biometric systems, their advancement and applications in different fields. Methodology: The core principles of the research issue have been well discussed in the literature review on speech biometrics. During this process, selected journals from a variety of secondary data sources, such as research papers published in a variety of reputed journals periodicals that are related to the topic are studied in the methodology. Findings/Result: A vocal biometric system is a biological system that captures an individual's voice and assigns it a unique characteristic for authentication purposes. This speech biometric method is primarily used to provide secure, quick, and frictionless access to various electronic devices. In the last three years, rapid technological advancements in neural networks have improved the deployment of speech biometric systems in a variety of industries. The majority of speech biometric system designs are based on the CPU, necessary power, and memory concepts. The advancement of software and hardware interface has been dramatically enhanced and implemented for many applications in the last few years, including smart watches, mobile phones, and car locking systems, where the interface between humans and electronics devices is critical. Banking security, attendance system, file access system, security control, and forensic development system are some of the other commercial applications. Originality: Following the literature study, the findings were utilized to conclude that, despite advances in biometric technology, there is still a significant gap in practical application, particularly for voice biometric systems. When building and developing a voice biometric system, it is necessary to integrate it with an IoT system. Paper Type: Literature Review.

Voice Biometrics for User Authentication

2012

Voice biometrics for user authentication is a task in which the goal is to perform convenient, robust and secure authentication of speakers. In this work we investigate the use of state-of-theart text-independent and text-dependent speaker verification technology for user authentication. We evaluate three different authentication conditions: global digit strings, speaker specific digit stings and prompted digit strings. Harnessing the characteristics of the different types of conditions can provide benefits such as authentication transparent to the user (convenience), spoofing robustness (security) and improved accuracy (reliability). The systems were evaluated on a corpus collected by Wells Fargo Bank which consists of 750 speakers. We show how to adapt techniques such as joint factor analysis (JFA), i-vectors, Gaussian mixture models with nuisance attribute projection (GMM-NAP) and hidden Markov models with NAP (HMM-NAP) to obtain improved results for new authentication scenarios ...

IDENTITY AUTHENTICATION USING VOICE BIOMETRICS TECHNIQUE

Identification of people using name, appearances, badges, tags and register may be effective may be in a small organization. However, as the size of the organization or society increases, these simple ways of identifying individual become ineffective. Therefore, it may be necessary to employ additional and more sophisticated means of authenticating the identity of people as the population increases. Voice Biometrics is a method by which individuals can be uniquely identified by evaluating one or more distinguishing biological traits associated with the voice of such individuals. In this paper, an unconstrained text-independent recognition system using the Gaussian Mixture Model was applied to match recorded voice to stored voice for the purpose of identification of individual. Recorded voices were processed and stored in the enrollment phase while probing voices were used for comparison in the verification/recognition phase of the system.

Voice Based Biometric System Feature Extraction Using MFCC and LPC Technique

— Now a day, interest in using biometric technologies for person authentication in security systems has grown rapidly.Voice is one of the most promising and mature biometric modalities for secured access control this paper gives an experimental overview of techniques used for feature extraction in speaker recognition. The research in speaker recognition have been evolved starting from short time features reflecting spectral properties of speech low-level or physical traits to the high level features (behavioral traits) such as prosody, phonetic information, conversational patterns etc. first give a brief overview of Speech processing and voice biometric relation and then describe some feature extraction technique. We have performed experiment for feature extraction of MFCC, LPC techniques.

Analysis of Speech Recognition and Voice Characterization

2017

Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific person's voices or it can be used to authenticate or verify the identity of a speaker as part of a security process. This work discusses the Implementation of an Enhanced Speaker Recognition system using MFCC and LBG Algorithm. MFCC has been used extensively for purposes of Speaker Recognition. This work has augmented the existing work by using Vector Quantization and Classification using the Linde Buzo Gray Algorithm. A complete test system has been developed in MATLAB which can be used for real time testing as it can take inputs directly from the Microphone. Therefore, the design can be translated into a Hardware having the necessary real time processing Prerequisites. The system has been tested using the VID TIMIT Database and using the Performance metrics of False Acceptance Rate (FAR), True Acceptance Rate (TAR) and False Rejection Rate(FRR). The system has been...

New Developments in Voice Biometrics for User Authentication

Voice biometrics for user authentication is a task in which the object is to perform convenient, robust and secure authentication of speakers. In this work we investigate the use of state-of-the-art text-independent and text-dependent speaker verification technology for user authentication. We evaluate four different authentication conditions: speaker specific digit stings, global digit strings, prompted digit strings, and text-independent. Harnessing the characteristics of the different types of conditions can provide benefits such as authentication transparent to the user (convenience), spoofing robustness (security) and improved accuracy (reliability). The systems were evaluated on a corpus collected by Wells Fargo Bank which consists of 750 speakers. We show how to adapt techniques such as joint factor analysis (JFA), Gaussian mixture models with nuisance attribute projection (GMM-NAP) and hidden Markov models with NAP (HMM-NAP) to obtain improved results for new authentication scenarios and environments

Application of Speaker Recognition on Biometric

Speaker recognition is the process of determining which registered speaker provides a given utterance followed by the process of accepting or rejecting the identity claim of a speaker. This paper reports on an experimental study involving signal processing in both time and frequency domain, and to receive a small bit of insight into the principles of speech analysis. This was accomplished by recording four speech segments from each person in our classroom, all of them varying slightly. Comparisons and analysis were then made on each signal, depending upon the instructions given by Dr. Qi.

A Review on User Identification using Voice as a Biometric Feature

2018

In this paper, we provide a concise overview for the user identification using his biometric featurespeech. Voice processing has multiple fields of research and is widely used in many applications. Speaker recognition to identify user is a complex process in which various techniques (feature extraction, feature matching, and identification) is used to match varied characteristics of voice between training and testing data to identify the user. This paper aims to discuss efficient method to implement the identification of user on basis of their biometric featurespeech.

Speech Authenticated Biometric System Using Neural Networks

This paper provides a method for biometric authentication using voice for speaker recognition. In the current day and age, security has become a major concern, only passwords aren't sufficient to ensure data safety. Hence it has become necessary to incorporate biometric features into security systems. The paper uses Mel-Frequency Cepstral Coefficients (MFCC) as the unique features for speaker as well as speech recognition. Neural Networks (NN) have been used for classification of input voice samples as authenticated or non-authenticated and to identify the word spoken. This method has been implemented using a rover, which uses this biometric authenticated system. The rover gets powered on only when the ―Rover Start‖ command received is from an authenticated user. Once initiated, the rover can execute direction commands such as ―Left‖, ―Right‖, ―Back‖, ―Forward‖, and ―Stop‖ given by any user.