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Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

The theories and applications of speaker identification, recognition, and verification are among the well-established fields. Many publications and advances in the relevant products are still emerging. In this paper, research-related publications of the past 25 years (from 1996 to 2020) were studied and analysed. Our main focus was on speaker identification, speaker recognition, and speaker verification. The study was carried out using the Science Direct databases. Several references, such as review articles, research articles, encyclopaedia, book chapters, conference abstracts, and others, were categorized and investigated. Summary of these kinds of literature is presented in this paper, together with statistical analyses to represent the publications and their categories over the mentioned period. Important information, including the dataset used, the size of the data adopted, the implemented methods, and the accuracy of the obtained results in the analysed research, are extracted from the explored publications and tabulated. The results show that the sum of published research articles is outnumbering other categories of publications. The number of researches in speech and speaker identification, recognition, and verification shows an increasing trend. Based on the normalized comparative factors of research publications, we found that many of them reached a high level of accuracy in their findings; hence the significantly superior techniques were derived and discussed for future researches. This survey paper would be beneficial for all those who wish to enhance their researches in the area of voice identification, recognition, and verification.

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Publication Date
Tue Dec 01 2009
Journal Name
Iraqi Journal Of Physics
Laplacian Operator as Speaker Identification Parameter

New speaker identification test’s feature, extracted from the differentiated form of the wave file, is presented. Differentiation operation is performed by an operator similar to the Laplacian operator. From the differentiated record’s, two parametric measures have been extracted and used as identifiers for the speaker; i.e. mean-value and number of zero-crossing points.

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Publication Date
Fri Dec 29 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Speaker Verification Using Hybrid Scheme for Arabic Speech

In this work , a hybrid scheme tor Arabic speech for the recognition

of  the speaker  verification  is presented  . The scheme is hybrid as utilizes the traditional digi tal signal processi ng and neural network . Kohonen neural  network has been used as a recognizer  tor speaker verification after extract spectral  features from an acoustic signal  by Fast Fourier Transformation Algorithm(FFT) .

The system was im plemented using a PENTIUM  processor , I000

MHZ compatible and MS-dos 6.2 .

 

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Review of Automatic Speaker Profiling: Features, Methods, and Challenges

Automatic Speaker Profiling (ASP), is concerned with estimating the physical traits of a person from their voice. These traits include gender, age, ethnicity, and physical parameters. Reliable ASP has a wide range of applications such as mobile shopping, customer service, robotics, forensics, security, and surveillance systems.  Research in ASP has gained interest in the last decade, however, it was focused on different tasks individually, such as age, height, or gender. In this work, a review of existing studies on different tasks of speaker profiling is performed. These tasks include age estimation and classification, gender detection, height, and weight estimation This study aims to provide insight into the work of ASP, available dat

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Publication Date
Sat Nov 02 2019
Journal Name
Advances In Intelligent Systems And Computing
Spin-Image Descriptors for Text-Independent Speaker Recognition

Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated fro

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Publication Date
Sun Aug 01 2021
Journal Name
Bulletin Of Electrical Engineering And Informatics
Robust speaker verification by combining MFCC and entrocy in noisy conditions

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robu

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Publication Date
Sun Jan 02 2022
Journal Name
Advances In Science And Technology Research Journal
Vein Biometric Recognition Methods and Systems: A Review

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Publication Date
Wed May 03 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Security of Iris Recognition and Voice Recognition Techniques

  Recently, biometric technologies are used widely due to their improved security that decreases cases of deception and theft. The biometric technologies use physical features and characters in the identification of individuals. The most common biometric technologies are: Iris, voice, fingerprint, handwriting and hand print. In this paper, two biometric recognition technologies are analyzed and compared, which are the iris and sound recognition techniques. The iris recognition technique recognizes persons by analyzing the main patterns in the iris structure, while the sound recognition technique identifies individuals depending on their unique voice characteristics or as called voice print. The comparison results show that the resul

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Publication Date
Fri Aug 05 2016
Journal Name
Wireless Communications And Mobile Computing
A comparison study on node clustering techniques used in target tracking WSNs for efficient data aggregation

Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregati

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Publication Date
Mon Dec 31 2018
Journal Name
Journal Of Theoretical And Applied Information Technology
Fingerprints Identification and Verification Based on Local Density Distribution with Rotation Compensation

The fingerprints are the more utilized biometric feature for person identification and verification. The fingerprint is easy to understand compare to another existing biometric type such as voice, face. It is capable to create a very high recognition rate for human recognition. In this paper the geometric rotation transform is applied on fingerprint image to obtain a new level of features to represent the finger characteristics and to use for personal identification; the local features are used for their ability to reflect the statistical behavior of fingerprint variation at fingerprint image. The proposed fingerprint system contains three main stages, they are: (i) preprocessing, (ii) feature extraction, and (iii) matching. The preprocessi

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
Human Activity Recognition using Smartwatch and Smartphone: A Review on Methods, Applications, and Challenges

     Recently, Human Activity Recognition (HAR) has been a popular research field due to wide spread of sensor devices. Embedded sensors in smartwatch and smartphone enabled applications to use sensors in activity recognition with challenges for example, support of elderly’s daily life . In the aim of recognizing and analyzing human activity many approaches have been implemented in researches.  Most articles published on human activity recognition used a multi -sensors based methods where a number of sensors were tied on different positions on a human body which are not suitable for many users. Currently, a smartphone and smart watch device combine different types of sensors which present a new area for analysi

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