Journal articles: 'Speech biometrics'

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Sayoud, Halim. "Biometrics." International Journal of Technoethics 2, no. 1 (January 2011): 19–34. http://dx.doi.org/10.4018/jte.2011010102.

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The term biometrics is derived from the Greek words: bio (life) and metrics (to measure). “Biometric technologies” are defined as automated methods of verifying or recognizing the identity of a living person based on a physiological or behavioral characteristic. Several techniques and features were used over time to recognize human beings several years before the birth of Christ. Today, this research field has become very employed in many applications such as security applications, multimedia applications and banking applications. Also, many methods have been developed to strengthen the biometric accuracy and reduce the imposture errors by using several features such as face, speech, iris, finger vein, etc. From a security purpose and economic point of view, biometrics has brought a great benefit and has become an important tool for governments and institutions. However, citizens are expressing their thorough worry, which is due to the freedom limitations and loss of privacy. This paper briefly presents some new technologies that have recently been proposed in biometrics with their levels of reliability, and discusses the different social and ethic problems that may result from the abusive use of these technologies.

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Gupta, Vikas, Shashikant Garade, Shriyash Kothekar, Darshana Meshram, Shraddha Wankhede, and Prof Rajshri Pote. "An Investigative Study of Voice Functioned Smart Door Lock System." International Journal of Research Publication and Reviews 04, no. 01 (2023): 1920–24. http://dx.doi.org/10.55248/gengpi.2023.4154.

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This article discusses how speech recognition can improve security for persons and property while making door systems more accessible to people with impairments. Facial recognition, fingerprint scanning, and iris scanning are examples of popular biometric technologies. Based on features and qualities used to identify various people for the safety and security of their lives and property, these biometric identifiers are distinctive and one-of a-kind. Sadly, these biometrics are vulnerable to hacking. A pin or password can be cracked, a person's finger can be severed to perform a fingerprint scan, an eyeball can be removed to perform an iris scan, and a person's photo can be used to perform facial recognition. With the help of speech recognition biometrics technology, these obstacles can be reduced. Technology for voice biometrics is more precise, swifter, more practical. In order to give individuals a quick way to open their doors and simultaneously protect their safety and security, this research study intends to design a door access control system that makes use of voice recognition algorithms. The testing phase and the training phase are the two phases that make up the system. The attributes of a speech are extracted and stored in a database during the training phase. Using voice recognition algorithms and vocal models, the intents from a person's address would be derived during the testing phase. A user is given access if a match is detected.

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Kinge, Steve. "Freedom of speech: Using speech biometrics for user verification." Network Security 2007, no. 1 (January 2007): 12–14. http://dx.doi.org/10.1016/s1353-4858(07)70006-5.

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Dinesh, Archana, and K. Edet Bijoy. "Computations on Cipher Speech for Secure Biometrics." Procedia Computer Science 93 (2016): 774–81. http://dx.doi.org/10.1016/j.procs.2016.07.293.

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Teoh, Andrew Beng Jin, and Lu Leng. "Special Issue on Advanced Biometrics with Deep Learning." Applied Sciences 10, no. 13 (June 28, 2020): 4453. http://dx.doi.org/10.3390/app10134453.

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Moreno, L. C., and P. B. Lopes. "The Voice Biometrics Based on Pitch Replication." International Journal for Innovation Education and Research 6, no. 10 (October 31, 2018): 351–58. http://dx.doi.org/10.31686/ijier.vol6.iss10.1201.

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Authentication and security in automated systems have become very much necessary in our days and many techniques have been proposed towards this end. One of these alternatives is biometrics in which human body characteristics are used to authenticate the system user. The objective of this article is to present a method of text independent speaker identification through the replication of pitch characteristics. Pitch is an important speech feature and is used in a variety of applications, including voice biometrics. The proposed method of speaker identification is based on short segments of speech, namely, three seconds for training and three seconds for the speaker determination. From these segments pitch characteristics are extracted and are used in the proposed method of replication for identification of the speaker.

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Setiawan, Ariyono. "Pengenalan Bentuk dan Pola Suara bagi Anak Anak Penyandang Tuna Rungu." Jurnal Penelitian 3, no. 2 (June 4, 2018): 57–65. http://dx.doi.org/10.46491/jp.v3e2.38.57-65.

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IInability to speak for the Deaf child is a distinctive characteristic that makes it different from normal children. Children with normal hearing understand the language through hearing in the months before they start talking. Speech recognition or recognition of voice patterns in children Deaf as search forms grades suitability and appropriateness. the type of sound is a development from techniques and systems that enable the computer to accept input in the form of patterns spoken word so on get the value of the type of words approaches, and can be understood. This study proposes a solution in Method utilizes biometrics to recognize the type of sound patterns deaf children who will be in the skewer with the sound of a normal child. Biometric methods used in digital signal processing (in this case sound) in the form of discrete biometrics refers to the automatic identification of humans by psikological or basic human characteristic sound.

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Rukhiran, Meennapa, Sorapak Pukdesree, and Paniti Netinant. "Biometric Cloud Services for Web-Based Examinations." International Journal of Information Technology and Web Engineering 17, no. 1 (January 2022): 1–25. http://dx.doi.org/10.4018/ijitwe.299022.

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Biometric recognition may be used in conjunction with human authentication on a smartphone to improve accuracy, reliability, and simplicity, and to aid in fraud prevention and user authentication. While single biometric authentication addresses environmental degradation and sensor noise limitations, and the single point of failure scenario in biometric systems can result in more robust biometric systems, multimodal biometric authentication can improve the accuracy of identification and recognition. The purpose of this research is to propose a facial and speech authentication system that is cloud-based and supports a web-based examination approach. The system enables students' biometrics to be registered, students to be recognized, and student recognition results to be reported. The confusion matrix is used to compare the results of positive and negative detection in various ways, including accuracy score, precision value, and recall value. Adaptive multimodal biometric authentication should be designed and evaluated for further research using the optimal weights for each biometric.

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Cherif, Youssouf Ismail, and Abdelhakim Dahimene. "IMPROVED VOICE-BASED BIOMETRICS USING MULTI-CHANNEL TRANSFER LEARNING." IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS 15, no. 1 (October 7, 2020): 99–113. http://dx.doi.org/10.33965/ijcsis_2020150108.

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Identifying the speaker has become more of an imperative thing to do in the modern age. Especially since most personal and professional appliances rely on voice commands or speech in general terms to operate. These systems need to discern the identity of the speaker rather than just the words that have been said to be both smart and safe. Especially if we consider the numerous advanced methods that have been developed to generate fake speech segments. The objective of this paper is to improve upon the existing voice-based biometrics to keep up with these synthesizers. The proposed method focuses on defining a novel and more speaker adapted features by implying artificial neural networks and transfer learning. The approach uses pre-trained networks to define a mapping from two complementary acoustic features to a speaker adapted phonetic features. The complementary acoustics features are paired to provide both information about how the speech segments are perceived (type 1 feature) and produced (type 2 feature). The approach was evaluated using both a small and large closed-speaker data set. Primary results are encouraging and confirm the usefulness of such an approach to extract speaker adapted features whether for classical machine learning algorithms or advanced neural structures such as LSTM or CNN.

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Besacier, L., P. Mayorga, J. F. Bonastre, C. Fredouille, and S. Meignier. "Overview of compression and packet loss effects in speech biometrics." IEE Proceedings - Vision, Image, and Signal Processing 150, no. 6 (2003): 372. http://dx.doi.org/10.1049/ip-vis:20031033.

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Kaur, Harjinder, and Gaganpreet Kaur. "Cuckoo Search based Optimization for Multimodal Biometrics (Signature, Speech and Palmprint)." International Journal of Computer Applications 107, no. 18 (December 18, 2014): 28–32. http://dx.doi.org/10.5120/18852-0399.

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Bansal, Nancy, and Samanpreet Singh. "Fusion Speech and Face Biometrics Using Enhanced Version of Genetic Algorithm." International Journal of Advanced Engineering Research and Science 3, no. 8 (2016): 113–18. http://dx.doi.org/10.22161/ijaers.3.8.5.

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Abd Aljabar, Raya W., and Nidaa F. Hassan. "Encryption VoIP based on Generated Biometric Key for RC4 Algorithm." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 209–21. http://dx.doi.org/10.30684/etj.v39i1b.1755.

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Voice over Internet Protocol (VoIP) calls are susceptible to interfere at many points by many attackers, thus encryption considered an important part in keeping VoIP. In this paper, Encryption VoIP based on Generated Biometric Key for RC4 Algorithm is proposed to encrypt the voice data before transmitting it over the network. The system uses a stream algorithm based on RC4 encryption with the new method of biometrics based Key generation technique. This system has generated complex keys in offline phase which is formed depend on features extracted using Linear Discernment Analysis (LDA) from face images. The experimental work shows that the proposed system offers secrecy to speech data with voice cipher is unintelligible and the recovered voice has perfect quality with MSR equal to zero and PSNR equal to infinity.

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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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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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Singh, Satyanand. "The role of speech technology in biometrics, forensics and man-machine interface." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 281. http://dx.doi.org/10.11591/ijece.v9i1.pp281-288.

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<span lang="EN-US">Day by day Optimism is growing that in the near future our society will witness the Man-Machine Interface (MMI) using voice technology. Computer manufacturers are building voice recognition sub-systems in their new product lines. Although, speech technology based MMI technique is widely used before, needs to gather and apply the deep knowledge of spoken language and performance during the electronic machine-based interaction. Biometric recognition refers to a system that is able to identify individuals based on their own behavior and biological characteristics. Fingerprint success in forensic science and law enforcement applications with growing concerns relating to border control, banking access fraud, machine access control and IT security, there has been great interest in the use of fingerprints and other biological symptoms for the automatic recognition. It is not surprising to see that the application of biometric systems is playing an important role in all areas of our society. Biometric applications include access to smartphone security, mobile payment, the international border, national citizen register and reserve facilities. The use of MMI by speech technology, which includes automated speech/speaker recognition and natural language processing, has the significant impact on all existing businesses based on personal computer applications. With the help of powerful and affordable microprocessors and artificial intelligence algorithms, the human being can talk to the machine to drive and control all computer-based applications. Today's applications show a small preview of a rich future for MMI based on voice technology, which will ultimately replace the keyboard and mouse with the microphone for easy access and make the machine more intelligent.</span>

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EhKan, Phaklen, Timothy Allen, and Steven F. Quigley. "FPGA Implementation for GMM-Based Speaker Identification." International Journal of Reconfigurable Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/420369.

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In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.

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Ryumin, D., and A. A. Karpov. "PARAMETRIC REPRESENTATION OF THE SPEAKER’S LIPS FOR MULTIMODAL SIGN LANGUAGE AND SPEECH RECOGNITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 155–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-155-2017.

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In this article, we propose a new method for parametric representation of human’s lips region. The functional diagram of the method is described and implementation details with the explanation of its key stages and features are given. The results of automatic detection of the regions of interest are illustrated. A speed of the method work using several computers with different performances is reported. This universal method allows applying parametrical representation of the speaker’s lipsfor the tasks of biometrics, computer vision, machine learning, and automatic recognition of face, elements of sign languages, and audio-visual speech, including lip-reading.

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Sinha, G. R. "Indian sign language (ISL) biometrics for hearing and speech impaired persons: review and recommendation." International Journal of Information Technology 9, no. 4 (October 20, 2017): 425–30. http://dx.doi.org/10.1007/s41870-017-0049-0.

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Et.al, Shanthakumar H. C. "Performance Evolution of Face and Speech Recognition system using DTCWT and MFCC Features." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3395–404. http://dx.doi.org/10.17762/turcomat.v12i3.1603.

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Every activity in day-to-day life is required the need of mechanized automation for ensuring the security. The biometrics security system provides the automatic recognition of human by overcoming the traditional recognition methods like Password, Personal Identification Number and ID cards etc. The face recognition is a wide research with many applications. In the proposed work face recognition is carried out using DTCWT (Dual Tree Complex Wavelet Transform) integrated with predominant QFT (Quick Fourier Transform) and speech recognition is carried out using MFCC (Mel Frequency Cepstral Coefficients) algorithm. The distance formula is used for matching the test features and database features of the face and speech images. Performance variables such as EER, FRR, FAR and TSR are evaluated for person recognition

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Widodo, Yuwono Fitri, Sunardi Sunardi, and Adbul Fadlil. "Identifikasi Suara Pada Sistem Presensi Karyawan Dengan Metode Ekstraksi MFCC." J-SAKTI (Jurnal Sains Komputer dan Informatika) 3, no. 1 (March 4, 2019): 115. http://dx.doi.org/10.30645/j-sakti.v3i1.107.

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Biometrics is the study of patterns of characteristics to recognize or identify humans based on one or more parts of the human body, both chemical, physical, and behavioral characteristics, such as faces, fingerprints, sounds, hand geometry, or iris. Nowadays technology has developed using sound to be used as an application that facilitates humans. Voice identification process is very necessary to know the accuracy of the sound based on the characteristics possessed, because some humans have similarities in saying. In this study aims to determine the sound pattern based on speech. The method used for voice identification using the Mel-Frequency Cepstral Coefficients (MFCC) feature extraction method, is a feature extraction method that approaches the human hearing system and is able to recognize speech patterns.

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Kaur, Gagandeep, and Seema Baghla. "Speech Recognition using Cross Correlation Algorithm Intended for Noise Reduction." Asian Journal of Computer Science and Technology 7, no. 3 (November 5, 2018): 48–52. http://dx.doi.org/10.51983/ajcst-2018.7.3.1899.

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Biometrics is presently a buzzword in the domain of information security as it provides high degree of accuracy in identifying an individual. Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Rudimentary speech recognition software has a limited vocabulary of words and phrases, and it may only identify these if they are spoken very clearly. The research work is intended to build a GUI environment which would provide provisions to record the speech and would assist in multiplying the database. The research work is primarily focused to implement a system capable of recognizing a user’s speech and creating audio files that can be added up to create a dynamic template or database. The research work emphasizes on directly recording the spoken words avoiding the problems with use of microphone. On appropriate recording and removal of the noise, the best matched audio file from the template is recognized when an input is provided externally on the basis of graphs created by considering correlation.

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Mamyrbayev, O., A. Akhmediyarova, A. Kydyrbekova, N. O. Mekebayev, and B. Zhumazhanov. "BIOMETRIC HUMAN AUTHENTICATION SYSTEM THROUGH SPEECH USING DEEP NEURAL NETWORKS (DNN)." BULLETIN 5, no. 387 (October 15, 2020): 6–15. http://dx.doi.org/10.32014/2020.2518-1467.137.

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Biometrics offers more security and convenience than traditional methods of identification. Recently, DNN has become a means of a more reliable and efficient authentication scheme. In this work, we compare two modern teaching methods: these two methods are methods based on the Gaussian mixture model (GMM) (denoted by the GMM i-vector) and methods based on deep neural networks (DNN) (denoted as the i-vector DNN). The results show that the DNN system with an i-vector is superior to the GMM system with an i-vector for various durations (from full length to 5s). DNNs have proven to be the most effective features for text-independent speaker verification in recent studies. In this paper, a new scheme is proposed that allows using DNN when checking text using hints in a simple and effective way. Experiments show that the proposed scheme reduces EER by 24.32% compared with the modern method and is evaluated for its reliability using noisy data, as well as data collected in real conditions. In addition, it is shown that the use of DNN instead of GMM for universal background modeling leads to a decrease in EER by 15.7%.

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Chiroma, Haruna. "Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review." Journal of Artificial Intelligence and Systems 3, no. 1 (2021): 157–97. http://dx.doi.org/10.33969/ais.2021.31010.

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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Li, Dongdong, Yingchun Yang, and Weihui Dai. "Cost-Sensitive Learning for Emotion Robust Speaker Recognition." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/628516.

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In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

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Zaporowski, Szymon, Andrzej Czyzewski, and Bozena Kostek. "Audio feature optimization approach towards speaker authentication in banking biometric system." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A349. http://dx.doi.org/10.1121/10.0008549.

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Experiments were carried out using algorithms such as Principal Component Analysis, Feature Importance, and Recursive Parameter Elimination dentifying most meaningful Mel-Frequency Cepstral Coefficients representing speech excerpts prepared for their classification are presented and discussed. The parameterization was made using Mel Frequency Cepstral Coefficients, Delta MFCC and Delta MFCC. In the next stage, feature vectors were passed to the input of individual algorithms utilized to reduce the size of the vector by previously mentioned algorithms. The vectors prepared in this way have been used for classifying vocalic segments employing Artificial Neural Network (ANN) and Support Vector Machine (SVM). The classification results using both classifiers and methods applied for reducing the number of parameters were presented. The results of the reduction are also shown explicitly, by indicating parameters proven to be significant and those rejected by particular algorithms. Factors influencing the obtained results were considered, such as difficulties associated with obtaining the data set, and ts labeling. The broader context of banking biometrics research carried-out and the results obtained in this domain were also discussed. [Project No. POIR.01.01.01-0092/19 entitled: “BIOPUAP—A biometric cloud authentication system” is currently financed by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund.]

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D.S., Dinesh Kumar, and P. V. Rao. "Implementing and analysing FAR and FRR for face and voice recognition (multimodal) using KNN classifier." International Journal of Intelligent Unmanned Systems 8, no. 1 (October 4, 2019): 55–67. http://dx.doi.org/10.1108/ijius-02-2019-0015.

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Purpose The purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone. Design/methodology/approach In this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy. Findings The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent. Originality/value The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

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Jianan, Lyu, and Ashardi Abas. "Development of Human-Computer Interactive Interface for Intelligent Automotive." International Journal of Artificial Intelligence 7, no. 2 (December 7, 2020): 13–21. http://dx.doi.org/10.36079/lamintang.ijai-0702.134.

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The wide application of information technology and network technology in automobiles has made great changes in the Human-computer interaction. This paper studies the influence of Human-computer interaction modes on driving safety, comfort and efficiency based on physical interaction, touch screen control interaction, augmented reality, speech interaction and somatosensory interaction. The future Human-com-puter interaction modes such as multi-channel Human-computer interaction mode and Human-computer interaction mode based on biometrics and perception techno-logy are also discussed. At last, the method of automobile Human-computer interaction design based on the existing technology is proposed, which has certain guiding significance for the current automobile Human-computer interaction interface design.

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Ma, He, Yi Zuo, Tieshan Li, and C. L. Philip Chen. "Data-Driven Decision-Support System for Speaker Identification Using E-Vector System." Scientific Programming 2020 (June 29, 2020): 1–13. http://dx.doi.org/10.1155/2020/4748606.

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Recently, biometric authorizations using fingerprint, voiceprint, and facial features have garnered considerable attention from the public with the development of recognition techniques and popularization of the smartphone. Among such biometrics, voiceprint has a personal identity as high as that of fingerprint and also uses a noncontact mode to recognize similar faces. Speech signal-processing is one of the keys to accuracy in voice recognition. Most voice-identification systems still employ the mel-scale frequency cepstrum coefficient (MFCC) as the key vocal feature. The quality and accuracy of the MFCC are dependent on the prepared phrase, which belongs to text-dependent speaker identification. In contrast, several new features, such as d-vector, provide a black-box process in vocal feature learning. To address these aspects, a novel data-driven approach for vocal feature extraction based on a decision-support system (DSS) is proposed in this study. Each speech signal can be transformed into a vector representing the vocal features using this DSS. The establishment of this DSS involves three steps: (i) voice data preprocessing, (ii) hierarchical cluster analysis for the inverse discrete cosine transform cepstrum coefficient, and (iii) learning the E-vector through minimization of the Euclidean metric. We compare experiments to verify the E-vectors extracted by this DSS with other vocal features measures and apply them to both text-dependent and text-independent datasets. In the experiments containing one utterance of each speaker, the average accuracy of the E-vector is improved by approximately 1.5% over the MFCC. In the experiments containing multiple utterances of each speaker, the average micro-F1 score of the E-vector is also improved by approximately 2.1% over the MFCC. The results of the E-vector show remarkable advantages when applied to both the Texas Instruments/Massachusetts Institute of Technology corpus and LibriSpeech corpus. These improvements of the E-vector contribute to the capabilities of speaker identification and also enhance its usability for more real-world identification tasks.

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MOHAMED, SOLTANE. "Product of Likelihood Ratio Scores Fusion of Face, Speech and Signature Based FJ-GMM for Biometrics Authentication Application Systems." Mathematics and Computer Science 2, no. 5 (2017): 51. http://dx.doi.org/10.11648/j.mcs.20170205.11.

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Singh Dhingra, Harkamal, and Dr Parveen Kakkar. "A REAL-TIME FACE RECOGNITION ATTENDANCE SYSTEM BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND SINGULAR VALUE DECOMPOSITION." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (March 1, 2022): 130–38. http://dx.doi.org/10.33564/ijeast.2022.v06i11.025.

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Attendance is an essential component of every organization. Keeping an attendance register daily is a challenging and time-consuming task. There are numerous automated ways available such as Biometrics, Eye Detection, Speech Recognition, etc. for human verifications. This paper outlines a simple and effective way for tracking attendance. Face recognition provides an accurate system that solves ambiguities such as fraudulent attendance, excessive cost, and time consumption. For facial identification and attendance storage, this system employs a face recognition library in Open CV (Python). The camera captures the image and sends it to a database folder containing images, which identify faces and calculate attendance. The goal of creating this automated attendance system utilizing Artificial Intelligence was to reduce the errors that occur in the traditional attendancetaking system. A face recognition system has been presented that has robustness toward user recognition and the result is transformed into an Excel Sheet in Real-Time

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Manu Pratap Singh and Reena Garg. "Techniques of deep learning for diagnosing brain diseases: A review." World Journal of Advanced Engineering Technology and Sciences 6, no. 2 (July 30, 2022): 001–25. http://dx.doi.org/10.30574/wjaets.2022.6.2.0072.

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Deep learning has been proved as a tremendous evolution in machine learning and computer vision. Through deep learning, computer machines become capable of solving the problems those were beyond the imagination two decades prior. Nowadays, Deep learning is significantly used in Natural language processing, image classification, medical science, handwriting recognition, face recognition, speech recognition, biometrics matching and in various real life problem domains. In this present paper, a review of deep learning techniques is presented for the diagnoses of brain diseases. Today, Deep learning is playing a crucial role in automating the medical equipment for the diagnosis of various brain diseases like tumor, Alzheimer, Mild Cognitive Impairment, brain hemorrhage, Parkinson etc. Deep learning has been tremendously used in detecting the severity of such diseases. This paper covers the recent approaches, techniques, learning algorithms of deep learning those have been used to detect major or minor diseases in a human brain. The paper also explores the future possibilities for Deep learning in medical science specifically for the brain diseases.

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Umar, Rusydi, Imam Riadi, and Abdullah Hanif. "Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)." CogITo Smart Journal 4, no. 2 (January 16, 2019): 294. http://dx.doi.org/10.31154/cogito.v4i2.130.294-304.

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Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC

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Ali, Noaman M., Abdullah Alshahrani, Ahmed M. Alghamdi, and Boris Novikov. "Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User." Applied Sciences 12, no. 13 (June 23, 2022): 6378. http://dx.doi.org/10.3390/app12136378.

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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Kumar, Vinod, and Om Prakash Roy. "Formant Measure of Indian English Vowels for Speaker Identity." Journal of Physics: Conference Series 2236, no. 1 (March 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2236/1/012011.

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Abstract With the growth of communication technology, reliability and security of communication devices became a challenging job. Voice biometrics are becoming increasingly popular as a promising alternative to traditional passwords to authenticate the user on devices for secure communication. Speech processing technology in voice identification may ensures and secure the user access over a range of systems, devices, and services. The formant frequency is the resonant frequency of the vocal cord. Frame-to-frame formants achieved using linear predictive coding (LPC) analysis technique by obtaining the tops of the envelopes. The source voice for vowel is a periodic signal in combination of fundamental frequency and a random noise generator generates unvoiced consonants. In this study, we used voice test samples of different male speakers from age 15 to 20 years. From each speaker utterance of different Indian English words, including vowels and consonants recorded using a digital audio editor software GoldWave v6.57. Formant frequency extracted from the spectrogram of recorded words using MATLAB 2016a signal processing toolbox. Finally, presented an approach for extracting vowels in the words spoken based on three formant frequencies such F1, F2 and F3. The results has shown the significance of vowels in Indian English words when formant frequency of vocal tract is considered.

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Fong, Simon, Kun Lan, and Raymond Wong. "Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection." BioMed Research International 2013 (2013): 1–27. http://dx.doi.org/10.1155/2013/720834.

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Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers’ gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC.

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Pusztahelyi, Réka, and Ibolya Stefán. "Household Social Robots − Special Issues Relating to Data Protection." Acta Universitatis Sapientiae, Legal Studies 11, no. 1 (June 15, 2022): 95–118. http://dx.doi.org/10.47745/ausleg.2022.11.1.06.

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Household social robots may have massive effects on our everyday lives and raise several concerns on data protection and privacy. The main characteristic of these devices is their capability of building close connections, even emotional bonds between humans and robots. The socially interactive robots exhibit human social characteristics, e.g. express and/or perceive emotions, communicate with high-level dialogue, etc. Affective computing permits development of AI systems that are capable of imitating human traits (emotions, speech, body language). The goal is to gain the trust of humans, to improve safety, and to strengthen emotional bonds between human and robot with the help of anthropomorphization. However, this emotional engagement may incentivize people to trade personal information jeopardizing their privacy. Social robots can infer from emotional expressions and gestures the feelings, physical and mental states of human beings. As a result, concerns may be raised regarding data protection, such as the classification of emotions, the issues of consent, and appearance of the right to explanation. The article proceeds in two main stages. The first chapter deals with general questions relating to emotional AI and social robots, focusing on the deceptive and manipulative nature that makes humans disclose more and more information and lull their privacy and data protection awareness. The second chapter serves to demonstrate several data protection problems such as the categorization and datafication of emotions (as biometrics), the issues of consent, and the appearance of the right to explanation. The third chapter highlights certain civil liability concerns regarding the infringement of the right to privacy in the light of the future EU civil liability regime for artificial intelligence.

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Colucci, Dennis A. "Biometric Audiology." Hearing Journal 68, no. 6 (June 2015): 40. http://dx.doi.org/10.1097/01.hj.0000466873.97809.47.

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Abushariah, Mohammad A. M., and Assal A. M. Alqudah. "Automatic Identity Recognition Using Speech Biometric." European Scientific Journal, ESJ 12, no. 12 (April 28, 2016): 43. http://dx.doi.org/10.19044/esj.2016.v12n12p43.

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Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recognizer using speech biometric technology based on Artificial Intelligence (AI) techniques provided in MATLAB. For our phase one, speech data is collected from 20 (10 male and 10 female) participants in order to develop the recognizer. The speech data include utterances recorded for the English language digits (0 to 9), where each participant recorded each digit 3 times, which resulted in a total of 600 utterances for all participants. For our phase two, speech data is collected from 100 (50 male and 50 female) participants in order to develop the recognizer. The speech data is divided into text-dependent and text-independent data, whereby each participant selected his/her full name and recorded it 30 times, which makes up the text-independent data. On the other hand, the text-dependent data is represented by a short Arabic language story that contains 16 sentences, whereby every sentence was recorded by every participant 5 times. As a result, this new corpus contains 3000 (30 utterances * 100 speakers) sound files that represent the text-independent data using their full names and 8000 (16 sentences * 5 utterances * 100 speakers) sound files that represent the text-dependent data using the short story. For the purpose of our phase one of developing the automatic identity recognizer using speech, the 600 utterances have undergone the feature extraction and feature classification phases. The speech-based automatic identity recognition system is based on the most dominating feature extraction technique, which is known as the Mel-Frequency Cepstral Coefficient (MFCC). For feature classification phase, the system is based on the Vector Quantization (VQ) algorithm. Based on our experimental results, the highest accuracy achieved is 76%. The experimental results have shown acceptable performance, but can be improved further in our phase two using larger speech data size and better performance classification techniques such as the Hidden Markov Model (HMM).

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Debnath, Saswati, and Pinki Roy. "User Authentication System Based on Speech and Cascade Hybrid Facial Feature." International Journal of Image and Graphics 20, no. 03 (July 2020): 2050022. http://dx.doi.org/10.1142/s0219467820500229.

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With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.

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Inthavisas, K., and N. Sungprasert. "Security Evaluation on Speech Biometric Authentication System." Advanced Materials Research 717 (July 2013): 826–31. http://dx.doi.org/10.4028/www.scientific.net/amr.717.826.

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A recent work utilized a transformation function to protect a DTW template. Unfortunately, a matching template was not protected properly. In this paper, we first show that an adversary can exploit the matching template to gain access to the system. Then, we introduce our scheme to address this problem. For this scheme, a hardened template is utilized to protect the DTW template. For the matching template, it is protected by a cryptographic framework. We evaluate the system with a public database: the MIT mobile device speaker verification corpus. The experimental results show that our scheme outperforms the other approaches.

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Inthavisas, K., and D. Lopresti. "Secure speech biometric templates for user authentication." IET Biometrics 1, no. 1 (2012): 46. http://dx.doi.org/10.1049/iet-bmt.2011.0008.

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Baldwin, Jacob, Ryan Burnham, Andrew Meyer, Robert Dora, and Robert Wright. "Beyond Speech: Generalizing D-Vectors for Biometric Verification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 842–49. http://dx.doi.org/10.1609/aaai.v33i01.3301842.

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Deep learning based automatic feature extraction methods have radically transformed speaker identification and facial recognition. Current approaches are typically specialized for individual domains, such as Deep Vectors (D-Vectors) for speaker identification. We provide two distinct contributions: a generalized framework for biometric verification inspired by D-Vectors and novel models that outperform current stateof-the-art approaches. Our approach supports substitution of various feature extraction models and improves the robustness of verification tests across domains. We demonstrate the framework and models for two different behavioral biometric verification problems: keystroke and mobile gait. We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains. Our models perform verification with higher accuracy using orders of magnitude less data than state-of-the-art approaches in both domains. We believe that the combination of high accuracy and practical data requirements will enable application of behavioral biometric models outside of the laboratory in support of much-needed improvements to cyber security.

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Thanki, Rohit M., and Komal Rajendrakumar Borisagar. "Securing Multiple Biometric Data Using SVD and Curvelet-Based Watermarking." International Journal of Information Security and Privacy 12, no. 4 (October 2018): 35–53. http://dx.doi.org/10.4018/ijisp.2018100103.

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The security and privacy of biometric data in multibiometric systems has become a hot research topic. In this paper, a singular value decomposition (SVD) and fast discrete curvelet transform (FDCuT)-based watermarking scheme for authenticity of fingerprint image using watermark speech signal has been proposed and analyzed. This scheme also provides security to watermark speech signal, which is inserted into the fingerprint image. This proposed scheme has a number of steps including fingerprint image authentication using watermark speech signal. The human speech signal is taken as secret watermark information and inserting into the human fingerprint image in the proposed scheme. The singular value of high frequency curvelet coefficients of the host fingerprint image is modified according to watermark speech signal to get secured and watermarked fingerprint image. The analysis results show that the performance of fingerprint recognition system is not affected by inserted watermark speech signal into host fingerprint image.

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Et. al., Gizachew Belayneh Gebre. "Artificial Neural Network Based Amharic Language Speaker Recognition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 5105–16. http://dx.doi.org/10.17762/turcomat.v12i3.2043.

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In this artificial intelligence time, speaker recognition is the most useful biometric recognition technique. Security is a big issue that needs careful attention because of every activities have been becoming automated and internet based. For security purpose, unique features of authorized user are highly needed. Voice is one of the wonderful unique biometric features. So, developing speaker recognition based on scientific research is the most concerned issue. Nowadays, criminal activities are increasing day to day in different clever way. So, every country should have strengthen forensic investigation using such technologies. The study was done by inspiration of contextualizing this concept for our country. In this study, text-independent Amharic language speaker recognition model was developed using Mel-Frequency Cepstral Coefficients to extract features from preprocessed speech signals and Artificial Neural Network to model the feature vector obtained from the Mel-Frequency Cepstral Coefficients and to classify objects while testing. The researcher used 20 sampled speeches of 10 each speaker (total of 200 speech samples) for training and testing separately. By setting the number of hidden neurons to 15, 20, and 25, three different models have been developed and evaluated for accuracy. The fourth-generation high-level programming language and interactive environment MATLAB is used to conduct the overall study implementations. At the end, very promising findings have been obtained. The study achieved better performance than other related researches which used Vector Quantization and Gaussian Mixture Model modelling techniques. Implementable result could obtain for the future by increasing number of speakers and speech samples and including the four Amharic accents.

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Thulasimani, L. "Text Dependent Speech based Biometric for Mobile Security." International Journal of Computer Applications 51, no. 17 (August 30, 2012): 35–40. http://dx.doi.org/10.5120/8136-1879.

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Kaur, Mandeep, Akshay Girdhar, and Manvjeet Kaur. "Multimodal Biometric System Using Speech and Signature Modalities." International Journal of Computer Applications 5, no. 12 (August 10, 2010): 13–16. http://dx.doi.org/10.5120/962-1339.

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Shukla, Anupam, Ritu Tiwari, and Chandra Prakash Rathore. "Neuro-fuzzy-based biometric system using speech features." International Journal of Biometrics 2, no. 4 (2010): 391. http://dx.doi.org/10.1504/ijbm.2010.035452.

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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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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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Kumar, Babu, Ajay Vikram Singh, and Parul Agarwal. "A Novel Approach for Speech to Text Recognition System Using Hidden Markov Model." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 31, 2022): 181–90. http://dx.doi.org/10.17762/ijritcc.v10i12.5934.

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Speech recognition is the application of sophisticated algorithms which involve the transforming of the human voice to text. Speech identification is essential as it utilizes by several biometric identification systems and voice-controlled automation systems. Variations in recording equipment, speakers, situations, and environments make speech recognition a tough undertaking. Three major phases comprise speech recognition: speech pre-processing, feature extraction, and speech categorization. This work presents a comprehensive study with the objectives of comprehending, analyzing, and enhancing these models and approaches, such as Hidden Markov Models and Artificial Neural Networks, employed in the voice recognition system for feature extraction and classification.

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Ziyatbekova, Gulzat, Magzhan Aliaskar, Aisha Abjalilova, Diana Montaeva, and Arailym Turlybekova. "BIOMETRIC IDENTIFICATION OF A PERSON BY SEVERAL PARAMETERS." Herald of Kazakh-British technical university 18, no. 2 (June 1, 2021): 39–44. http://dx.doi.org/10.55452/1998-6688-2021-18-2-39-44.

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The article is devoted to the development of a system of biometric identification of a person by face,fingerprints and voice. Two-dimensional and three-dimensional characteristics of a person's face, taking into account area and volume, were used as informative signs of biometric identification of a person by face. A complex identification algorithm has been developed to account for such phenomena as portrait shift, different photo scales, and the tilt of the identified face. The FPM10A scanner and the Arduino microcontroller are used for biometric identification of a person by fingerprints. Identification signs are based on the analysis of the structure of papillary patterns on the finger: type and type of papillary pattern; direction and steepness of streams of papillary lines; the structure of the central pattern of the pattern; delta structure; the number of papillary lines between the center and the delta and many other signs. Another type of feature is local. They are also called minutiae (features or special points) — unique features inherent only in a particular print, determining the points of change in the structure of papillary lines (end, split, break, etc.), the orientation of papillary lines and coordinates at these points. Each print can contain up to 70 or more minutations. For biometric identification of a person by voice, MFC and PLP algorithms for digital processing and analysis of audio recordings are used. Various algorithms are used for acoustic speech analysis: hidden Markov models, a model of a mixture of Gaussian distributions. The result of determining the tone of speech and the content of speech for the purposes of voice identification is obtained. The Visual FoxPro DBMS has developed a «multiparametric automated system for biometric identification of an individual».

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Journal articles on the topic 'Speech biometrics'

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