Готові списки джерел за темами / Voice biometry

Добірка наукової літератури з теми "Voice biometry"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Voice biometry"

Lutsenko, K., and K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON." Theory and Practice of Forensic Science and Criminalistics 19, no. 1 (April 2, 2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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Анотація:

The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the corresponding classes is given. The main advantages of voice biometrics such as simplicity of system realization are considered; low cost (the lowest among all biometric methods); No need for contact, the voice biometry allows for long-range verification, unlike other biometric technologies. The analysis of existing methods of speech recognition recognition identifying a person by a combination of unique voice characteristics, determining their weak and strong points, on the basis of which the choice of the most appropriate method for solving the problem of text-independent recognition, Namely the model of Gaussian mixtures, was carried out. The prerequisite for the development of speech technologies is a significant increase in computing capabilities, memory capacity with a significant reduction in the size of computer systems. It should also be Noted the development of mathematical methods that make it possible to perform the Necessary processing of an audio signal by isolating informative features from it. It has been established that the development of information technologies, and the set of practical applications, which use voice recognition technologies, make this area relevant for further theoretical and practical research.

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., Himanshi, Trisha Gulati, and Yasha Hasija. "Biometrics in Healthcare." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 3, no. 2 (April 15, 2018): 13–17. http://dx.doi.org/10.35121/ijapie201804223.

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Анотація:

Biometrics is the discipline to measure physical human characteristics for the identification and authentication of an individual. Since ancient times, people have used voice, face, and other characteristics for the identification of an individual. With evolution, we take the individual characteristics like fingerprint scans, retina and iris images, etc., as inputs to the computer systems and then store or verify them with existing records. This report discusses biometrics and its recent roles found in the field of healthcare, medicine, genetics, and biotechnology. It includes the concept of biometrics, the system used for biometric recognition and its working, types of biometric systems, the different system algorithms applied, and system modules which are well illustrated with flow charts and block diagrams. Some of the health institutes in developed countries have started using biometric systems for checking patients and/or doctors. Biometry has enabled the proper organization and storage of the health records of individuals in medical institutes. Biometric authentication is also finding a distinct role in foiling medical claims fraud highlighting the advantages it. Even after processing via a very accurate biometric system, there is a chance of a false result due to some disease or injury to the body part subjected to biometry or faulty system leading to some error. There is also a possibility that the biometric system may harm our bodies. Moreover, biometric records need really tight system security to prevent any kind of misuse. Biometrics has a great potential to find a lot more uses in the field of healthcare. Many ideas are being proposed for implementation. In the future, biometrics can be used to detect potential disease and risks by using methods like adiposity measurement and Gas Discharge Visualization (GDV).

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Chinyemba, Melissa K., and Jackson Phiri. "Gaps in the Management and Use of Biometric Data: A Case of Zambian Public and Private Institutions." Zambia ICT Journal 2, no. 1 (June 29, 2018): 35–43. http://dx.doi.org/10.33260/zictjournal.v2i1.49.

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Анотація:

The current physical and cybersecurity systems rely on traditional three-factor authentication to mitigate the threats posed by insider attacks. Key is the use of biometric information. Biometrics are a unique measurement and analysis of the unique physiological special traits such as voice, eye structure and others that can be used in the discipline of varying person identification. Biometry, which is the analysis of these biometrics is a complex process but guarantees identification and non-repudiation. If used to identify humans then several issues such as where is the biometric data stored? Who has access to it? And how does one ensure that such data satisfies the principle of availability. To achieve availability, secure transportation arises. To achieve transportation, non-repudiation, confidentiality and authentication, integrity arise. A storage and transport system is recommended to these challenges. In this paper, we explore the gaps into how public and private institution store and manage biometrics information. We benchmarked each organization again the ISO 30107 and ISO 24745. Our results show that while most companies are adopting and using biometrics systems, few have adopted the ISO biometrics standards that govern the storage and management of biometric information and hence creating security risk.

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Adhinata, Faisal Dharma, Diovianto Putra Rakhmadani, and Alon Jala Tirta Segara. "Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM." Journal of Dinda : Data Science, Information Technology, and Data Analytics 1, no. 1 (February 2, 2021): 28–33. http://dx.doi.org/10.20895/dinda.v1i1.198.

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Анотація:

Biometric information that exists in humans is unique from one human to another. One of the biometric data that is easily obtained is the human voice. The human voice is identic data that can differentiate between individuals. When we hear human voices directly, it is easy for our ears to tell the person who is speaking is male or female. But sometimes male voices can resemble girls and vice versa. Therefore, we propose a human voice detection system through Artificial Intelligence (AI) in machine learning. In this study, we used the Mel Frequency Cepstrum Coefficients (MFCC) method to extract human voice features and Gaussian Mixture Models (GMM) for the classification of female or male voice data. The experiment results showed that the system built was able to detect human gender through biometric voice data with an accuracy of 81.18%.

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Ouamour, Siham, and Halim Sayoud. "Speaker Discrimination on Broadcast News and Telephonic Calls Using a Fusion of Neural and Statistical Classifiers." International Journal of Mobile Computing and Multimedia Communications 1, no. 4 (October 2009): 47–63. http://dx.doi.org/10.4018/jmcmc.2009072804.

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This article describes a new Speaker Discrimination System (SDS), which is a part of an overall project called Audio Documents Indexing based on a Speaker Discrimination System (ADISDS). Speaker discrimination consists in checking whether two speech segments come from the same speaker or not. This research domain presents an important field in biometry, since the voice remains an important feature used at distance (via telephone). However, although some discriminative classifiers do exist nowadays, their performances are not enough sufficient for short speech segments. This issue led us to propose an efficient fusion between such classifiers in order to enhance the discriminative performance. This fusion is obtained, by using three different techniques: a serial fusion, parallel fusion and serial-parallel fusion. Also, two classifiers have been chosen for the evaluation: a mono-gaussian statistical classifier and a Multi Layer Perceptron (MLP). Several experiments of speaker discrimination are conducted on different databases: Hub4 Broadcast-News and telephonic calls. Results show that the fusion has efficiently improved the scores obtained by each approach alone. So, for instance, we got an Equal Error Rate (EER) of about 7% on a subset of Hub4 Broadcast-News database, with short segments of 4 seconds, and an EER of about 4% on telephonic speech, with medium segments of 10 seconds.

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Singh, Nilu, Alka Agrawal, and R. A. Khan. "Voice Biometric: A Technology for Voice Based Authentication." Advanced Science, Engineering and Medicine 10, no. 7 (July 1, 2018): 754–59. http://dx.doi.org/10.1166/asem.2018.2219.

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Algabri, Mohammed, Hassan Mathkour, Mohamed A. Bencherif, Mansour Alsulaiman, and Mohamed A. Mekhtiche. "Automatic Speaker Recognition for Mobile Forensic Applications." Mobile Information Systems 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/6986391.

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Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.

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Abdalrahman, Roaya Salhalden A., Bülent Bolat, and Nihan Kahraman. "A cascaded voice biometric system." Procedia Computer Science 131 (2018): 1223–28. http://dx.doi.org/10.1016/j.procs.2018.04.334.

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Park, Hyun, and TaeGuen Kim. "User Authentication Method via Speaker Recognition and Speech Synthesis Detection." Security and Communication Networks 2022 (January 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/5755785.

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Анотація:

As the Internet has been developed, various online services such as social media services are introduced and widely used by many people. Traditionally, many online services utilize self-certification methods that are made using public certificates or resident registration numbers, but it is found that the existing methods pose the risk of recent personal information leakage accidents. The most popular authentication method to compensate for these problems is biometric authentication technology. The biometric authentication techniques are considered relatively safe from risks like personal information theft, forgery, etc. Among many biometric-based methods, we studied the speaker recognition method, which is considered suitable to be used as a user authentication method of the social media service usually accessed in the smartphone environment. In this paper, we first propose a speaker recognition-based authentication method that identifies and authenticates individual voice patterns, and we also present a synthesis speech detection method that is used to prevent a masquerading attack using synthetic voices.

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Mamyrbayev, Orken, Aizat Kydyrbekova, Keylan Alimhan, Dina Oralbekova, Bagashar Zhumazhanov, and Bulbul Nuranbayeva. "Development of security systems using DNN and i & x-vector classifiers." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (August 31, 2021): 32–45. http://dx.doi.org/10.15587/1729-4061.2021.239186.

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Анотація:

The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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Більше джерел

Дисертації з теми "Voice biometry"

Lee, Samuel K. "Proof of concept : Iraqi enrollment via voice authentication project /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FLee%5FSamuel.pdf.

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Thesis (M.S. in InformationTechnology Management)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): James F. Ehlert, Pat Sankar. Includes bibliographical references (p.271-272). Also available online.

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Грушко, Ярослав Володимирович. "Система голосової біометрії, економна до обчислювальних ресурсів". Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/32176.

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Мета даної роботи – створити економну до обчислювальних ресурсів систему голосової біометрії. Основною ціллю роботи стали побудова загальної схеми такої системи, визначення її компонент та оптимальних параметрів. Об’єктом дослідження даної магістерської дипломної роботи є розпізнавання голосу людини комп’ютером. Предмет дослідження – голосова біометрія, тобто голосове розпізнавання особи. Спроєктована система складається з трьох основних модулів. Перший модуль – це алгоритм отримання голосового відбитку MFCCs. Другий модуль – це класифікатор, який має навчатися голосовими відбитками отриманими за допомогою першого модуля. Третій, і останній, модуль є верифікатором, який вдруге (після класифікатора) перевіряє правильність визначення особи. Задля підбору параметрів було розроблено окрему систему. Виходячи з підібраних оптимальних параметрів було створено консольний додаток голосової біометрії на мові програмування python та окремий мобільний додаток на java. Точність консольного додатку на вибірці 80 зразків 40-ка різних дикторів склала 93%. При проходженні аутентифікації, коли оброблювалося 6 секунд промови, тривалість роботи консольного додатку склала 2 секунди. Виконано перший етап розроблення стартап-проєкту, а саме, виконано маркетинговий аналіз стартап-проекту.
The purpose of this work is to create a cost-effective system for voice biometrics. The main purpose of the work was to build a general scheme of such a system as well as determine its components and optimal parameters. The object of study of this master's work is the recognition of human voice by computer. The subject of the study is voice biometrics, ie voice recognition of the individual. Designed system contain three basic modules. The first module is the MFCCs, the algorithm that give off individual voiceprint. The second module is a classifier that has to learn the voiceprints obtained with the first module. The third, and last, module is the verifier, which for the second time (after the classifier) verifies the correct identification of the person. A separate system was developed for parameter selection. Based on the selected optimal parameters, console application of voice biometrics in the Python programming language and a separate java mobile application were created. The accuracy of the console application on a dataset of 80 samples of 40 different individuals was 93%. During authentication, when 6 seconds of speech were been processing, the duration of the console application working was 2 seconds. The first stage of the development of the startup project was completed, namely, the marketing analysis of the startup project was performed.

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Atah, Alewo Joshua. "Strategies for template-free direct biometric encryption using voice based features." Thesis, University of Kent, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544079.

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Rouse, Kenneth Arthur Gilbert Juan E. "Classifying speakers using voice biometrics In a multimodal world." Auburn, Ala, 2009. http://hdl.handle.net/10415/1824.

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Fransson, Linda, and Therese Jeansson. "Biometric methods and mobile access control." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5023.

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Our purpose with this thesis was to find biometric methods that can be used in access control of mobile access. The access control has two parts. Firstly, to validate the identity of the caller and, secondly, to ensure the validated user is not changed during the session that follows. Any solution to the access control problem is not available today, which means that anyone can get access to the mobile phone and the Internet. Therefore we have researched after a solution that can solve this problem but also on how to secure that no one else can take over an already validated session. We began to search for biometric methods that are available today to find them that would be best suited together with a mobile phone. After we had read information about them we did choose three methods for further investigation. These methods were Fingerprint Recognition, Iris Scan and Speaker Verification. Iris Scan is the method that is best suited to solve the authentication problem. The reasons for this are many. One of them is the uniqueness and stability of the iris, not even identical twins or the pair of the same individual has the same iris minutiae. The iris is also very protected behind eyelids, cornea and the aqueous humor and therefore difficult to damage. When it comes to the method itself, is it one of the most secure methods available today. One of the reasons for this is that the equal error rate is better than one in a million. However, this rate can be even better. It all depends on the Hamming Distance, which is a value that show how different the saved and temporarily template are, and what it is set to. To solve our session authentication, which was to make sure that no one else could take over a connected mobile phone, a sensor plate is the answer. This sensor will be able to sense for touch, heat and pulse. These three sensor measurements will together secure a validated session since the mobile phone will disconnect if the sensor looses its sensor data. There are, however, technological and other challenges to be solved before our proposed solutions will become viable. We address some of these issues in our thesis.

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Firc, Anton. "Použitelnost Deepfakes v oblasti kybernetické bezpečnosti." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445534.

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Deepfake technológia je v poslednej dobe na vzostupe. Vzniká mnoho techník a nástrojov pre tvorbu deepfake médií a začínajú sa používať ako pre nezákonné tak aj pre prospešné činnosti. Nezákonné použitie vedie k výskumu techník pre detekciu deepfake médií a ich neustálemu zlepšovaniu, takisto ako k potrebe vzdelávať širokú verejnosť o nástrahách, ktoré táto technológia prináša. Jedna z málo preskúmaných oblastí škodlivého použitia je používanie deepfake pre oklamanie systémov hlasovej autentifikácie. Názory spoločnosti na vykonateľnosť takýchto útokov sa líšia, no existuje len málo vedeckých dôkazov. Cieľom tejto práce je preskúmať aktuálnu pripravenosť systémov hlasovej biometrie čeliť deepfake nahrávkam. Vykonané experimenty ukazujú, že systémy hlasovej biometrie sú zraniteľné pomocou deepfake nahrávok. Napriek tomu, že skoro všetky verejne dostupné nástroje a modely sú určené pre syntézu anglického jazyka, v tejto práci ukazujem, že syntéza hlasu v akomkoľvek jazyku nie je veľmi náročná. Nakoniec navrhujem riešenie pre zníženie rizika ktoré deepfake nahrávky predstavujú pre systémy hlasovej biometrie, a to používať overenie hlasu závislé na texte, nakoľko som ukázal, že je odolnejšie proti deepfake nahrávkam.

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Assaad, Firas Souhail. "Biometric Multi-modal User Authentication System based on Ensemble Classifier." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418074931.

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Válková, Jana. "Formy zadávání a zpracování textových dat a informací v podnikových IS - trendy a aktuální praxe." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-114263.

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This thesis introduces readers to the basic types of the text and information inputs and processing to the computer. Thesis also includes historical contexts, current trends and future perspective of computer data input technologies and their use in practice. The first part of the thesis is a summary of a particular forms of entering and processing of the text data and information. The following part presents technological trends on the market concentrated on the automatic speech recognition systems along with the possibilities of their application in the business sphere. The rest of the thesis consists of a survey between Czech IT companies and based on it's results comes a suggestion of which technologies should be used as a part of the information systems.

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Sanderson, Conrad, and conradsand@ieee org. "Automatic Person Verification Using Speech and Face Information." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030422.105519.

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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the persons speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.

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Тодорів, Андрій Дмитрович. "Система багатофакторної аутентифікації користувачів комп’ютерних систем". Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/38366.

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Вирішення проблеми захисту корпоративних даних в ХХІ столітті вийшло за рамки фізичної взаємодії з працівниками, у зв’язку з переходом шуканої інформації в комп’ютерний формат. Дана особливість сформувала потребу у розробці та імплементації нових механізмів захисту корпоративних даних. Запропонована система аутентифікації користувачів комп’ютерних систем, розроблена на основі технологій нейронних мереж, надає можливість ідентифікації користувачів на основі індивідуальних антропометричних візуальних та голосових показників суб’єкта, з метою запобігання викраденню корпоративних даних, та ідентифікації злочинних суб’єктів. Об’єктом дослідження є трансформація антропометричних показників в комп’ютерну форму. Предметом дослідження є механізми розпізнавання образів. Метою роботи є покращення можливостей методів біометричної ідентифікації суб’єктів шляхом розробки нової архітектури на базі нейронних мереж. Методи дослідження. Порівняння існуючих алгоритмів за критеріями точності, швидкодії, ресурсних затрат, надійності, з метою імплементації та подальшої модифікації в системі корпоративного контролю. Наукова новизна полягає у розробці нового механізму ідентифікації суб’єктів що поєднує у собі алгоритми голосової та візуальної ідентифікації суб’єктів. Практична цінність полягає у можливості застосування даної системи в корпоративних умовах з метою запобігання витоку даних та ідентифікації злочинних суб’єктів. Низька ресурсозатратність сприяє застосуванню розробленого алгоритму в високонавантажених системах. Структура та обсяг роботи. Магістерська дисертація складається з вступу, чотирьох розділів, висновків та додатків. У вступі аналізується проблема захисту корпоративних даних. Обгрунтовується перспективність застосування механізмів біометричної голосової та візуальної ідентифікації суб’єктів для її вирішення. Досліджуються алгоритми біометричної ідентифікації. У першому розділі описуються існуючі алгоритми розпізнавання візуальних та голосових образів. У другому розділі досліджується доцільність застосування існуючих алгоритмів голосової та візуальної біометричної ідентифікації, аналізуються та порівнюються існуючі архітектури розпізнавання образів. У третьому розділі наводиться процес розробки алгоритмів візуальної та голосової біометричної ідентифікації користувачів У четвертому розділі наводяться характеристики розробленої КС, результати тестування, відбувається дослідження системи на різних наборах даних, та її модифікація з метою досягнення поставленої точності. У висновках стисло наводяться результати досліджень та розробки.
Topic relevance The solution to the problem of corporate data protection in the XXI century has gone beyond the physical interaction with employees, due to the transition of the required information into a computer format. This feature has formed the need to develop and implement new mechanisms for corporate data protection. The proposed system of authentication of computer system users, developed on the basis of neural network technologies, provides the possibility of user identification on the basis of individual anthropometric visual and voice indicators of the subject, in order to prevent theft of corporate data and identification of criminal entities. The object of study is the transformation of anthropometric indicators into a computer form. The subject of study is the mechanisms of pattern recognition. The goal of this work is to improve the capabilities of biometric identification methods of subjects by developing a new architecture based on neural networks. Study methods. Comparison of existing algorithms on the criteria of accuracy, speed, resource costs, reliability, in order to implement and further modify the corporate control system. The scientific novelty is the development of a new mechanism for identifying subjects that combines algorithms for voice and visual identification of subjects. The practical value lies in the possibility of using this system in a corporate environment in order to prevent data leakage and identification of criminal entities. Low resource consumption contributes to the application of the developed algorithm in highly loaded systems. Structure and scope of work. The master's dissertation consists of an introduction, four chapters, conclusions and appendices. The introduction analyzes the problem of corporate data protection. The prospects of using the mechanisms of biometric voice and visual identification of subjects for its solution are substantiated. Biometric identification algorithms are investigated. The first section describes the existing algorithms for recognizing visual and voice images. The second section investigates the feasibility of using existing algorithms for voice and visual biometric identification, analyzes and compares existing image recognition architectures. The third section describes the process of developing algorithms for visual and voice biometric user identification The fourth section presents the characteristics of the developed COP, the test results, the system is studied on different data sets, and its modification in order to achieve the specified accuracy. The conclusions summarize the results of research and development.

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Книги з теми "Voice biometry"

HOW DOES VOICE RECOGNITION WORK? New York, NY: Gareth Stevens Publishing, 2014.

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High-Tech Ids: From Finger Scans to Voice Patterns. Franklin Watts, 2000.

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Tocci, Salvatore. High-Tech Ids: From Finger Scans to Voice Patterns. Tandem Library, 2001.

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Tocci, Salvatore. High-Tech IDs: From Finger Scans to Voice Patterns. Turtleback Books Distributed by Demco Media, 2000.

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Tocci, Salvatore. High-Tech Ids: From Finger Scans to Voice Patterns (Single Title: Science). Franklin Watts, 2000.

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Частини книг з теми "Voice biometry"

Lupu, E., and M. Cioban. "Voice Biometric System." In IFMBE Proceedings, 239–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04292-8_53.

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Wankhede, N. S. "Voice-Based Biometric Authentication." In Nanoelectronics, Circuits and Communication Systems, 229–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7486-3_22.

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Farrell, Kevin, Scott Sharp, and Ron Beyner. "Voice Biometrics for Securing Your Web-Based Business." In Biometric Solutions, 377–92. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1053-6_14.

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Wang, Houying, and Weiping Hu. "Optimization of Pathological Voice Feature Based on KPCA and SVM." In Biometric Recognition, 394–403. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12484-1_44.

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Zhao, Qiming, Yingchun Yang, and Hong Li. "A Novel and Efficient Voice Activity Detector Using Shape Features of Speech Wave." In Biometric Recognition, 375–84. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12484-1_42.

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Ortega-Garcia, Javier, Joaquin Gonzalez-Rodriguez, Danilo Simon-Zorita, and Santiago Cruz-Llanas. "From Biometrics Technology to Applications Regarding Face, Voice, Signature and Fingerprint Recognition Systems." In Biometric Solutions, 289–337. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1053-6_12.

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Himawan, Ivan, Srikanth Madikeri, Petr Motlicek, Milos Cernak, Sridha Sridharan, and Clinton Fookes. "Voice Presentation Attack Detection Using Convolutional Neural Networks." In Handbook of Biometric Anti-Spoofing, 391–415. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_17.

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Přibilová, Anna, and Jiří Přibil. "Harmonic Model for Female Voice Emotional Synthesis." In Biometric ID Management and Multimodal Communication, 41–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04391-8_6.

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Batra, Neera, and Sonali Goyal. "Text-Independent Voice Biometric for User Recognition." In Lecture Notes in Mechanical Engineering, 799–806. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9956-9_77.

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Sahidullah, Md, Héctor Delgado, Massimiliano Todisco, Tomi Kinnunen, Nicholas Evans, Junichi Yamagishi, and Kong-Aik Lee. "Introduction to Voice Presentation Attack Detection and Recent Advances." In Handbook of Biometric Anti-Spoofing, 321–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_15.

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Тези доповідей конференцій з теми "Voice biometry"

Vilda, Pedro Gomez, Victoria Rodellar Biarge, Cristina Munoz Mulas, Rafael Martinez Olalla, Luis M. Mazaira Fernandez, and Agustin Alvarez Marquina. "Glottal parameter estimation by wavelet transform for voice biometry." In 2011 International Carnahan Conference on Security Technology (ICCST). IEEE, 2011. http://dx.doi.org/10.1109/ccst.2011.6095951.

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Pires, Fabio, Eduardo Mario Dias, Miguel Edgar Morales Udaeta, and Felippe da Silva Pires. "INTELLIGENT VEHICLE SAFETY SYSTEM FOR FACIAL BIOMETRY AND VOICE TO REDUCE TRANSIT ACCIDENTS." In XXVI Simpósio Internacional de Engenharia Automotiva. São Paulo: Editora Blucher, 2018. http://dx.doi.org/10.5151/simea2018-pap60.

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Sadkhan, Sattar B., Baheeja K. Al-Shukur, and Ali K. Mattar. "Biometric voice authentication auto-evaluation system." In 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT). IEEE, 2017. http://dx.doi.org/10.1109/ntict.2017.7976100.

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Motwani, Rakhi C., Sergiu M. Dascalu, and Frederick C. Harris. "Voice biometric watermarking of 3D models." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5485658.

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Silva, Lucas Gomes da, Álan L. V. Guedes, and Sérgio Colcher. "Using Deep Learning to Recognize People by Face and Voice." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8143.

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Анотація:

There are many ways to build a person identification system and those systems can be used for authentication and security. The latest phones for example, bring fingerprint readers to enhance the user experience. From our perspective on Neural Networkswe determine that Machine Learning is enough to guarantee someone’s identity without the need of any specific sensors other than a camera and a microphone. It is achievable with pictures of their face, sounds of their voice and Deep Learning. This work presents a study to build an application to allow biometric authentication using only multimedia and Deep Learning.

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Hernández-Trapote, Álvaro, Beatriz López-Mencía, David Díaz, Rubén Fernández-Pozo, and Javier Caminero. "Embodied conversational agents for voice-biometric interfaces." In the 10th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1452392.1452454.

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Camlikaya, Eren, Alisher Kholmatov, and Berrin Yanikoglu. "Multi-biometric templates using fingerprint and voice." In SPIE Defense and Security Symposium, edited by B. V. K. Vijaya Kumar, Salil Prabhakar, and Arun A. Ross. SPIE, 2008. http://dx.doi.org/10.1117/12.777738.

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Burks, William Garret, Paola Jaramillo, and Alexander Leonessa. "Development of Electromagnetic Stimulation System to Aid Patients Suffering From Vocal Fold Paralysis." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14562.

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Vocal fold paralysis affects approximately 7.5 million Americans. Paralysis can be caused by numerous conditions, including head, neck or surgical trauma, endotracheal intubation, neurological conditions, cancer, tumors, just to mention a few. Currently, vocal fold paralysis treatment involves surgery and voice therapy. The vocal folds are composed of a three part material stretched along the larynx, which enables frequency change. Intrinsic laryngeal muscles coordinate the motion of vocal folds during respiration, vocalization, and aid in airway protection. Sensory information is carried by the Superior Laryngeal Nerve (SLN) and the Recurrent Laryngeal Nerve (RLN). Injury to the RLN results in paralysis of all laryngeal muscles excluding the cricothyroid muscle [1]. Although optimal larynx reinnervation has been extensively researched and implemented to improve voice paralysis [2], voice electrotherapy offers an alternative to effectively stimulate the larynx muscles for voice production, breathing and airway protection. One of the main causes of voice disorders is neurological in nature and causes abnormal vocal fold vibration. Of particular importance to this research is paralysis due to RLN injury, which causes acute temporary paralysis [3]. Currently, invasive electrical stimulus is used to activate muscle function; however, abnormal activation of muscle patterns causes muscles to function out of synchronization resulting in low vocal output [4]. For this reason, our work focuses on the development of an effective electromagnetic stimulation system to aid patients with unilateral vocal fold paralysis by stimulating the RLN and in turn reinnervating the adequate laryngeal muscles involved in the vocal fold motion for the purposes of sound vocalization, respiration, and airway protection. So far, a proof of principle has been developed and evaluated to assess the system’s feasibility. The preliminary experiments have been conducted using BioMetal Fibers (BMF) (Toki Corporation, Japan), which are fiber-like solid state actuators designed to contract and extend similar to muscles. BMF contracts when stimulated through a current generated in this case through an electromagnetic field.

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Chetty, Girija, and Michael Wagner. "Multi-Level Liveness Verification for Face-Voice Biometric Authentication." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341615.

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Barbu, Tudor, Adrian Ciobanu, and Mihaela Luca. "Multimodal biometric authentication based on voice, face and iris." In 2015 E-Health and Bioengineering Conference (EHB). IEEE, 2015. http://dx.doi.org/10.1109/ehb.2015.7391373.

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