Rozprawy doktorskie na temat „Speech biometrics”
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Sanderson, Conrad, i 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.
Pełny tekst źródłaSanderson, Conrad. "Automatic Person Verification Using Speech and Face Information". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/367191.
Pełny tekst źródłaThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
Full Text
Rouse, Kenneth Arthur Gilbert Juan E. "Classifying speakers using voice biometrics In a multimodal world". Auburn, Ala, 2009. http://hdl.handle.net/10415/1824.
Pełny tekst źródłaKotulek, Milan. "Jednoduchý textově nezávislý hlasový zámek - Softwarový systém pro verifikaci mluvčích". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221256.
Pełny tekst źródłaMelin, Håkan. "Automatic speaker verification on site and by telephone: methods, applications and assessment". Doctoral thesis, KTH, Tal, musik och hörsel, TMH, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4242.
Pełny tekst źródłaQC 20100910
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.
Pełny tekst źródłaBoško, Božilović. "Биометријско обележје за препознавање говорника: дводимензионална информациона ентропија говорног сигнала". Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2016. http://www.cris.uns.ac.rs/record.jsf?recordId=101369&source=NDLTD&language=en.
Pełny tekst źródłaMotiv za istraživanje je unapređenje procesa automatskog prepoznavanja govornika bez obzira na sadržaj izgovorenog teksta.Cilj ove doktorske disertacije je definisanje novog biometrijskog obeležja za prepoznavanje govornika nezavisno od izgovorenog teksta − dvodimenzionalne informacione entropije govornog signala.Definisanje novog obeležja se vrši isključivo u vremenskom domenu, pa je računarska složenost algoritma za njegovo izdvajanje znatno manja u odnosu na obeležja koja se izdvajaju u frekvencijskom domenu. Ocena performansi dvodimenzionalne informacione entropije je urađena nad reprezentativnim skupom slučajno odabranih govornika. Pokazano je da predloženo obeležje ima malu varijabilnost unutar govornog signala jednog govornika, a veliku varijabilnost između govornih signala različitih govornika.
Тhe motivation for the research is the improvement of the automatic speaker recognition process regardless of the content of spoken text.The objective of this dissertation is to define a new biometric text-independent speaker recognition feature − the two-dimensional informational entropy of speech signal.Definition of the new feature is performed in time domain exclusively, so the computing complexity of the algorithm for feature extraction is significantly lower in comparison to feature extraction in spectral domain. Performance analysis of two-dimensional information entropy is performed on the representative set of randomly chosen speakers. It has been shown that new feature has small within-speaker variability and significant between-speaker variability.
Chan, Siu Man. "Improved speaker verification with discrimination power weighting /". View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20CHANS.
Pełny tekst źródłaIncludes bibliographical references (leaves 86-93). Also available in electronic version. Access restricted to campus users.
Vlasenko, Andrej. "Studentų emocinės būklės testavimo metu tyrimas panauduojant biometrines technologijas". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120329_153219-37955.
Pełny tekst źródłaThe dissertation investigates the issues of creating a computer system that uses voice signal features to determine person’s emotional state. In addition pre-sented system of measuring pupil diameter.The main objects of research include emotion recognition from speech and dynamics of eye pupil size change.The main purpose of this dissertation is employing suitable methodologies and algo-rithms to automatically process and analyse human voice parameters. Created algorithms can be used in Stress Management System software. The dissertation also focuses on researching the possibilities of identification of speaker’s psy-choemotional state: applying the analysis of speaker’s voice parameters and the analysis of dynamics of eye pupil size change. The dissertation consists of four parts including Introduction, 4 chapters, Conclusions and References. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the paper, re-search methodology, scientific novelty, the practical significance of results ex-amined in the paper and defended statements. The introduction ends in present-ing the author’s publications on the subject of the defended dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1- the Recommended Biometric Stress Management System found-ed on the speech analysis. The System can assist in determining the level of... [to full text]
Hartung, Karin. "Biometrical approaches for analysing gene bank evaluation data on barley (Hordeum spec.)". [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:100-opus-2251.
Pełny tekst źródłaJagadeesan, Harini. "Design and Verification of Privacy and User Re-authentication Systems". Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/32394.
Pełny tekst źródłaBoth keyboard and mouse contain valuable, hard-to-duplicate information about the userâ s behavior. This can be used for analysis and identification of the current user. We propose an application independent system that uses this information for user re-authentication. This system will authenticate the user continually based on his/her behavioral attributes obtained from both the keyboard and mouse operations. This re-authentication system is simple, continual, non-intrusive and easily deployable. To utilize the mouse and keyboard information for re-authentication, we propose a novel heuristic that uses the percentage of mouse-to-keyboard interaction ratio. This heuristic allows us to extract suitable user-behavioral attributes. The extracted data is compared with an already trained database for user re-authentication.
The accuracy of the system is calculated by the number of correct identifications to total number of identifications. At present, the accuracy of the system is around 96% for application based user re-authentication and around 82% for application independent user re-authentication. We perform black box, white box testing and Spec# verification procedures that prove the robustness of the proposed system. On testing POCKET, a privacy protection software for children, it was found that the security of POCKET was inadequate at the user level. Our system enhances POCKET security at the user level and ensures that the childâ s privacy is protected.
Master of Science
Mekyska, Jiří. "Identifikace osob pomocí otisku hlasu". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218235.
Pełny tekst źródłaKahn, Juliette. "Parole de locuteur : performance et confiance en identification biométrique vocale". Phd thesis, Université d'Avignon, 2011. http://tel.archives-ouvertes.fr/tel-00995071.
Pełny tekst źródłaFabík, Vojtěch. "Fantomy pro oftalmologický ultrazvukový systém". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220047.
Pełny tekst źródłaLEE, CHIEN-PENG, i 李建鵬. "Multi-modal Presentation Attacks Detection based on Mouth Dynamic and Speech Biometrics". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u68nz3.
Pełny tekst źródła國防大學
網路安全碩士班
107
Biometric technologies have been widely used in daily life due to the advancement of information technology today. However, biometrics still have a high risks of being deceived. For example, an imposter pretends to be a legitimate user illegally accessing the system. This study proposed a countermeasure for the “Video Attack” in face recognition system based on the multi-modal method combined with motion detection and speech recognition. The motion is detected in a continuous time based on the mouth aspect ratio (MAR) while the user is talking. The similarities between the talk and the recognized speech are compared. The score level fusion method is used to fuse these two features, and then the Decision Tree, Random Forest, k-Nearest Neighbor and Naïve Bayes classifiers are used to conduct classifying and testing in the experiments. Experimental results show the accuracy of the proposed method for Video Attack detection reaches as high as 95.17%. It also shows that the proposed multi-modal presentation attacks detection method can effectively improve face recognition system security.
Таванець, Назарій Станіславович, i Nazariy Tavanets. "Математичне моделювання мовних сигналів для задач біометричної ідентифікації користувачів". Master's thesis, 2022. http://elartu.tntu.edu.ua/handle/lib/37919.
Pełny tekst źródłaВСТУП……………………………………………………………………………10 1 СТАН ДОСЛІДЖЕНЬ В ОБЛАСТІ БІОМЕТРИЧНОЇ ІДЕНТИФІКАЦІЇ ТА АУТЕНТИФІКАЦІЇ……………………………………………………………...13 1.1 Суть біометричної ідентифікації та аутентифікації………………….13 1.2 Традиційні методи ідентифікації……………………………………...14 1.3 Переваги біометричної ідентифікації…………………………………16 1.4 Окремі методи біометричної ідентифікації…………………………..18 1.4.1 Розпізнавання відбитків пальців……………………………..18 1.4.2 Розпізнавання обличчя……………………………………..…19 1.4.3 Розпізнавання райдужної оболонки…………………………20 1.4.4 Розпізнавання вен пальців……………………………………21 1.4.5 Розпізнавання образів долонної вени………………………..22 1.5 Основи ідентифікації за мовним сигналом…………………………...23 1.6 Суть та типи ідентифікації за мовним сигналом…………………..…25 1.7 Практики використання ідентифікації за мовними сигналами……...27 1.8 Переваги та недоліки ідентифікації за мовним сигналом…………...28 1.9 Висновки до розділу 1…………………………………………………30 2 ОБГРУНТУВАННЯ ВИБОРУ МАТЕМАТИЧНОЇ МОДЕЛІ МОВНИХ СИГНАЛІВ………………………………………………………………………32 2.1 Природа мовних сигналів…………………………………………..…32 2.2 Можливості подання мовних сигналів як стаціонарного випадкового процесу…………………………………………………………………..…41 2.3 Вибір математичної моделі мовних сигналів для задачі ідентифікації користувача…………………………………………………………………45 2.4 Висновки до розділу 2…………………………………………………46 3 РОЗРОБКА МЕТОДУ ІДЕНТИФІКАЦІЇ КОРИСТУВАЧІВ ЗА МОВНИМ СИГНАЛОМ……………………………………………………………………..48 3.1 Метод ідентифікації користувача за мовним сигналом……………..48 3.2 Перспективи використання розробленого методу…………………...60 3.3 Висновки до розділу 3…………………………………………………61 4 ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ…..62 4.1 Вимоги до приміщення та робочого місця при дослідженні мовного сигналу………………………………………………………………………62 4.2 Організація і функціонування системи управління охороною праці 69 ВИСНОВКИ……………………………………………………………………...74 ПЕРЕЛІК ВИКОРИСТАНИХ ДЖЕРЕЛ……………………………………….76
Wu, Dalei [Verfasser]. "Discriminative preprocessing of speech : towards improving biometric authentication / vorgelegt von Dalei Wu". 2007. http://d-nb.info/98472317X/34.
Pełny tekst źródłaAdamski, Michal Jerzy. "A speaker recognition solution for identification and authentication". Thesis, 2014. http://hdl.handle.net/10210/11317.
Pełny tekst źródłaA certain degree of vulnerability exists in traditional knowledge-based identification and authentication access control, as a result of password interception and social engineering techniques. This vulnerability has warranted the exploration of additional identification and authentication approaches such as physical token-based systems and biometrics. Speaker recognition is one such biometric approach that is currently not widely used due to its inherent technological challenges, as well as a scarcity of comprehensive literature and complete open-source projects. This makes it challenging for anyone who wishes to study, develop and improve upon speaker recognition for identification and authentication. In this dissertation, we condense some of the available speaker recognition literature in a manner that would provide a comprehensive overall picture of speaker identification and authentication to a wider range of interested audiences. A speaker recognition solution in the form of an open, user-friendly software prototype environment is presented, called SRIA (Speaker Recognition Identification Authentication). In SRIA, real users may enrol and perform speaker identification and authentication tasks. SRIA is intended as platform for speaker recognition understanding and further research and development.
"Text-independent speaker recognition using discriminative subspace analysis". 2012. http://library.cuhk.edu.hk/record=b5549636.
Pełny tekst źródła在先進的說話人識別系統中,每個說話人模型是通過給定的說話人數據進行特徵統計分佈估計由生成模型訓練得到。這類方法由於需要逐帧進行概率或似然度計算而得出最終判決,會耗費大量系統資源並降低實時性性能。採用子空間降維技術,我們不僅避免選取冗餘高維度數據,同時能夠有效删除於識別中無用之數據。為克服上述生成性模型的不足並獲得不同說話人間的區分邊界,本文提出了利用區分性子空間方法訓練模型並採用有效的距離測度作為最終的建模識別新算法。
在本篇論文中,我們將先介紹並分析各類產生性說話人識別方法,例如高斯混合模型及聯合因子分析。另外,為了降低特徵空間維度和運算時間,我們也對子空間分析技術做了調研。除此之外,我們提出了一種取名為Fishervoice 基於非參數分佈假定的新穎說話人識別框架。所提出的Fishervoice 框架的主要目的是為了降低噪聲干擾同時加重分類信息,而能夠加強在可區分性的子空間內對聲音特徵建模。採用上述Fishervoice 框架,說話人識別可以簡單地通過測試樣本映射到Fishervoice 子空間並計算其簡單歐氏距離而實現。為了更好得降低維度及提高識別率,我們還對Fishervocie 框架進行多樣化探索。另外,我們也在低維度的全變化空間(Total Variability) 對各類多種子空間分析模型進行調比較。基於XM2VTS 和NIST 公開數據庫的實驗驗證了本文提出的算法的有效性。
Speaker Recognition (SR), which uses the voice to determine the speaker’s identity, is an important and challenging research topic for biometric authentication. Generally speaking, speaker recognition can be divided into text-dependent and text-independent methods according to the verbal content of the speech signal. There are two major applications of speaker recognition: the first is speaker verification, also referred to speaker authentication, which is used to validate the identity of a speaker according to the voice and it involves a binary decision. The second is speaker identification, which is used to determine an unknown speaker’s identity.
In a state-of-art speaker recognition system, the speaker training model is usually trained by generative methods, which estimate feature distribution of each speaker among the given data. These generative methods need a frame-based metric (e.g. probability, likelihoods) calculation for making final decision, which consumes much computer resources, slowing down the real-time responses. Meanwhile, lots of redundant data frames are blindly selected for training without efficient subspace dimension reduction. In order to overcome disadvantages of generative methods and obtain boundary information between individual speakers, we propose to apply the discriminative subspace technique for model training and employ simple but efficient distance metrics for decision score calculation.
In this thesis, we shall present an overview of both conventional and state-of-the-art generative speaker recognition methods (e.g. Gaussian Mixture Model and Joint Factor Analysis) and analyze their advantages and disadvantages. In addition, we have also made an investigation of the application of subspace analysis techniques to reduce feature dimensions and computation time. After that, a novel speaker recognition framework based on the nonparametric Fisher’s discriminant analysis which we name Fishervoice is proposed. The objective of the proposed Fishervoice algorithm is to model the intrinsic vocal characteristics in a discriminant subspace for de-emphasizing unwanted noise variations and emphasizing classification boundaries information. Using the proposed Fishervoice framework, speaker recognition can be easily realized by mapping a test utterance to the Fishervoice subspace and then calculating the score between the test utterance and its reference. Besides, we explore the proposed Fishervoice framework with several extensions for further dimensionality reduction and performance improvement. Furthermore, we investigate various subspace analysis techniques in a total variability-based low-dimensional space for fast computation. Extensive experiments on two large speaker recognition corpora (XM2VTS and NIST) demonstrate significant improvements of Fishervoice over standard, state-of-the-art approaches for both speaker identification and verification systems.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Jiang, Weiwu.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2012.
Includes bibliographical references (leaves 127-135).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Abstract --- p.i
Acknowledgements --- p.vi
Contents --- p.xiv
List of Figures --- p.xvii
List of Tables --- p.xxiii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview of Speaker Recognition Systems --- p.1
Chapter 1.2 --- Motivation --- p.4
Chapter 1.3 --- Outline of Thesis --- p.6
Chapter 2 --- Background Study --- p.7
Chapter 2.1 --- Generative Gaussian Mixture Model (GMM) --- p.7
Chapter 2.1.1 --- Basic GMM --- p.7
Chapter 2.1.2 --- The Gaussian Mixture Model-Universal Background Model (GMM-UBM) System --- p.9
Chapter 2.2 --- Discriminative Subspace Analysis --- p.12
Chapter 2.2.1 --- Principal Component Analysis --- p.12
Chapter 2.2.2 --- Linear Discriminant Analysis --- p.16
Chapter 2.2.3 --- Heteroscedastic Linear Discriminant Analysis --- p.17
Chapter 2.2.4 --- Locality Preserving Projections --- p.18
Chapter 2.3 --- Noise Compensation --- p.20
Chapter 2.3.1 --- Eigenvoice --- p.20
Chapter 2.3.2 --- Joint Factor Analysis --- p.24
Chapter 2.3.3 --- Probabilistic Linear Discriminant Analysis --- p.26
Chapter 2.3.4 --- Nuisance Attribute Projection --- p.30
Chapter 2.3.5 --- Within-class Covariance Normalization --- p.32
Chapter 2.4 --- Support Vector Machine --- p.33
Chapter 2.5 --- Score Normalization --- p.35
Chapter 2.6 --- Summary --- p.39
Chapter 3 --- Corpora for Speaker Recognition Experiments --- p.41
Chapter 3.1 --- Corpora for Speaker Identification Experiments --- p.41
Chapter 3.1.1 --- XM2VTS Corpus --- p.41
Chapter 3.1.2 --- NIST Corpora --- p.42
Chapter 3.2 --- Corpora for Speaker Verification Experiments --- p.45
Chapter 3.3 --- Summary --- p.47
Chapter 4 --- Performance Measures for Speaker Recognition --- p.48
Chapter 4.1 --- Performance Measures for Identification --- p.48
Chapter 4.2 --- Performance Measures for Verification --- p.49
Chapter 4.2.1 --- Equal Error Rate --- p.49
Chapter 4.2.2 --- Detection Error Tradeoff Curves --- p.49
Chapter 4.2.3 --- Detection Cost Function --- p.50
Chapter 4.3 --- Summary --- p.51
Chapter 5 --- The Discriminant Fishervoice Framework --- p.52
Chapter 5.1 --- The Proposed Fishervoice Framework --- p.53
Chapter 5.1.1 --- Feature Representation --- p.53
Chapter 5.1.2 --- Nonparametric Fisher’s Discriminant Analysis --- p.55
Chapter 5.2 --- Speaker Identification Experiments --- p.60
Chapter 5.2.1 --- Experiments on the XM2VTS Corpus --- p.60
Chapter 5.2.2 --- Experiments on the NIST Corpus --- p.62
Chapter 5.3 --- Summary --- p.64
Chapter 6 --- Extension of the Fishervoice Framework --- p.66
Chapter 6.1 --- Two-level Fishervoice Framework --- p.66
Chapter 6.1.1 --- Proposed Algorithm --- p.66
Chapter 6.2 --- Performance Evaluation on the Two-level Fishervoice Framework --- p.70
Chapter 6.2.1 --- Experimental Setup --- p.70
Chapter 6.2.2 --- Performance Comparison of Different Types of Input Supervectors --- p.72
Chapter 6.2.3 --- Performance Comparison of Different Numbers of Slices --- p.73
Chapter 6.2.4 --- Performance Comparison of Different Dimensions of Fishervoice Projection Matrices --- p.75
Chapter 6.2.5 --- Performance Comparison with Other Systems --- p.77
Chapter 6.2.6 --- Fusion with Other Systems --- p.78
Chapter 6.2.7 --- Extension of the Two-level Subspace Analysis Framework --- p.80
Chapter 6.3 --- Random Subspace Sampling Framework --- p.81
Chapter 6.3.1 --- Supervector Extraction --- p.82
Chapter 6.3.2 --- Training Stage --- p.83
Chapter 6.3.3 --- Testing Procedures --- p.84
Chapter 6.3.4 --- Discussion --- p.84
Chapter 6.4 --- Performance Evaluation of the Random Subspace Sampling Framework --- p.85
Chapter 6.4.1 --- Experimental Setup --- p.85
Chapter 6.4.2 --- Random Subspace Sampling Analysis --- p.87
Chapter 6.4.3 --- Comparison with Other Systems --- p.90
Chapter 6.4.4 --- Fusion with the Other Systems --- p.90
Chapter 6.5 --- Summary --- p.92
Chapter 7 --- Discriminative Modeling in Low-dimensional Space --- p.94
Chapter 7.1 --- Discriminative Subspace Analysis in Low-dimensional Space --- p.95
Chapter 7.1.1 --- Experimental Setup --- p.96
Chapter 7.1.2 --- Performance Evaluation on Individual Subspace Analysis Techniques --- p.98
Chapter 7.1.3 --- Performance Evaluation on Multi-type of Subspace Analysis Techniques --- p.105
Chapter 7.2 --- Discriminative Subspace Analysis with Support Vector Machine --- p.115
Chapter 7.2.1 --- Experimental Setup --- p.116
Chapter 7.2.2 --- Performance Evaluation on LDA+WCCN+SVM --- p.117
Chapter 7.2.3 --- Performance Evaluation on Fishervoice+SVM --- p.118
Chapter 7.3 --- Summary --- p.118
Chapter 8 --- Conclusions and Future Work --- p.120
Chapter 8.1 --- Contributions --- p.120
Chapter 8.2 --- Future Directions --- p.121
Chapter A --- EM Training GMM --- p.123
Bibliography --- p.127
Hartung, Karin [Verfasser]. "Biometrical approaches for analysing gene bank evaluation data on barley (Hordeum spec.) / presented by Karin Hartung". 2008. http://d-nb.info/987648837/34.
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