PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

System kontroli dostępu oparty na biometrycznej weryfikacji głosu

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
EN
The concept of embedded solution for voice biometric access system
Języki publikacji
PL
Abstrakty
PL
Artykuł przedstawia koncepcję głosowego, biometrycznego systemu dostępowego zrealizowanego jako system wbudowany. Zaprezentowano najważniejsze wymagania dotyczące systemów kontroli dostępu oraz wynikające z nich założenia projektowe. Opisano architekturę utworzonego systemu, jego funkcjonalność oraz zastosowane metody weryfikacji mówcy wraz z omówieniem podstawowych metod optymalizacji czasowej implementacji. Całość poprzedzona jest zarysem zagadnienia biometrii głosu oraz automatycznego przetwarzania mowy.
EN
The paper presents the concept of embedded solution for voice biometric access system. The most important requirements for access control systems are presented, as well as the resulting design intent. The architecture of the created system, its functionality and the methods used to verify the speakers is described along with a discussion of basic time-optimization methods of implementation. The entirety is preceded by an outline of the issues of voice biometrics and automatic speech processing.
Rocznik
Strony
248--255
Opis fizyczny
Bibliogr. 38 poz., il., schem., tab., wykr.
Twórcy
autor
  • Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, Wydział Informatyki, Elektroniki i Telekomunikacji, Katedra Elektroniki, al. Mickiewicza 30, 30-059 Kraków
autor
  • Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, Wydział Informatyki, Elektroniki i Telekomunikacji, Katedra Elektroniki, al. Mickiewicza 30, 30-059 Kraków
autor
  • Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, Wydział Informatyki, Elektroniki i Telekomunikacji, Katedra Elektroniki, al. Mickiewicza 30, 30-059 Kraków
Bibliografia
  • [1] Norma PN-EN 50133-1:1996 + AC:1998 + A1:2002, Systemy alarmowe – Systemy Kontroli Dostępu – Część 1: Wymagania Systemowe
  • [2] Jain A. K., An Introduction to Biometric Recognition, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14 (2004), no. 1
  • [3] ITL Biometrics Overview, http://www.nist.gov/itl/biometrics/
  • [4] Maltoni D., Maio D., Jaiin A. K., Prabhakar S., Handbook of Fingerprint Recognition, IEEE NIST Fingerprint Evaluations and Developments, vol. 94, no. 11 (2006)
  • [5] Wang J., Yau W., Suwandy A., Sung E., Person Recognition by Fusing Palmprint and Palm Vein Images Based on ‘Laplacianpalm’ Representation, Pattern Recognition, vol. 41, issue5 (2008), 1514-1527
  • [6] Taigman Y., Yang M., Ranzato M., Wolf L., Closing the Gap to Human-Level Performance in Face Verification, Conference on Computer Vision and Pattern Recognition CVPR (2014)
  • [7] Kumar N., Berg A. C., Belhumeur P. N., Nayar S. K., Attribute and Simile Classifiers for Face Verification, ICCV (2009)
  • [8] Reynolds D., Rose R., Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models, IEEE Transactions on Speech and Audio Processing, vol. 3, no. 1 (1995)
  • [9] Kinnunen T., Li H., An Overview of Text-Independent Speaker Recognition: From Features to Supervectors, Speech Communication, 52 (2010), 12-40
  • [10] Campbell J., Speaker Recognition: A Tutorial, Proceedings of the IEEE, vol. 8, no. 9 (1997)
  • [11] Martin A., Greenberg C., The NIST 2010 Speaker Recognition Evaluation, INTERSPEECH 2010, p. 2726-2729
  • [12] Hebert M., Text-Depentent Speaker Recognition, Springer Handbook of Speech Processing (2008), 743-762
  • [13] Greenberg C., Martin A., Barr B., Report on Performance in the NIST 2010 Speaker Recognition Evaluation, INTERSPEECH 2011, 261-264
  • [14] Petrovska-Delacretaz D., Hennebert J., Text-Prompted Speaker Verification Experiments with Phoneme Specific MLP’s, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (1998), vol. 2, p. 777 - 780
  • [15] NIST Speaker Recognition Evaluation 2012, http://www.nist.gov/itl/iad/mig/sre12results.cfm
  • [16] Martin A., The DET Curve in Assessment of Detection Task Performance, Eurospeech (1997), vol. 4, p. 1899-1903
  • [17] Benesty J., Sondhi M., Huand Y., Springer Handbook of Speech Processing (2008)
  • [18] Heldner M., Edlund J., Pauses and Overlaps in Conversations, Journal of Phonetics (2010), vol. 38, issue 4, 555-568
  • [19] Robert F., Alexander L., Francis B., Morgan M., The Interaction of Inter-turn Silence with Prosodic Cues in Listener Perceptions of ‘Trouble’ in Conversation, Speech Communication 48 (2006), 1079-1093
  • [20] Mao P., Liu J., A Novel Embedded Speaker Verification on System on Chip, Sixth International Conference on Fuzzy Systems and Knowledge Discovery (2009)
  • [21] Moon Y. S., Leung C. C., Pun K. H., Fixed-point GMM-based Speaker Verification over Mobile Embedded System, WBMA (2003), Berkeley, California
  • [22] Leung C. C., Moon Y. S., Meng H., A Pruning Approach for GMM-Based Speaker Verification in Mobile Embedded Systems, Lecture Notes in Computer Science (2004), vol. 3072, p. 607 - 613
  • [23] Kramberger I., Grasic M., Rotovnik T., Door Phone Embedded System for a Voice-Based User Identification and Verification Platform, IEEE Transactions on Consumer Electronics (2011), vol. 57, issue 3
  • [24] FreeRTOSTM Project Homepage, www.freertos.org
  • [25] SL018 User Manual, StrongLink Homepage, www.stronglinkrfid.com
  • [26] MP45DT02 – MEMS audio sensor omnidirectional digital microphone Datasheet, www.st.com
  • [27] PDM Audio Software Decoding on STM32 Microcontrollers, Application Note AN3998, www.st.com
  • [28] Kinnunen T., Li H., An Overview of Text-Independent Speaker Recognition - From Features To Supervectors, Speech Communication 52 (2010), 12-40
  • [29] Kenny P., Boulianne G., Dumouchel P., Eigenvoice Modeling With Sparse Training Data, IEEE Transactions On Speech and Audio Processing (2005), Vol. 13, No. 3
  • [30] Munteanu D. P., Toma S. A., Automatic Speaker Verification Experiments using HMM, 8th International Conference on Communications (2010), p. 107 - 110
  • [31] Young S., Evermann G., Gales M., Hain T., Kershaw D., Liu X., et al, The HTK Book, Cambridge University Engineering Department (2009)
  • [32] Reynolds D., Gaussian Mixture Models, Encyclopedia of Biometrics (2009), 659-663
  • [33] Rabiner L., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE (1989), vol. 77, no. 2
  • [34] Auckenthaler R., Carey M., Lloyd-Thomas H., Score Normalization for Text-Independent Speaker Verification Systems, Digital Signal Processing 10 (2000), 42-54
  • [35] McLaughlin J., Reynolds D., Gleason T., A Study of Computation Speed-ups of the GMM-UBM Speaker Recognition System, Sixth European Conference on Speech Communication and Technology, EUROSPEECH (1999), Budapest, Hungary
  • [36] Mohammadi S., Saeidi R., Efficent Implementation of GMMBased Speaker Verification Using Sorted Gaussian Mixture Models, 14th European Signal Processing Conference, EUSIPCO 2006, Florence, Italy
  • [37] Cai J., Bouselmi G., Fohr D., Dynamic Gaussian Selection for Speeding Up HMM-Based Continuous Speech Recognition, International Conference on Acoustics and Signal Processing (2008), Las Vegas, USA
  • [38] Wan V., Renals S., Speaker Verification Using Sequence Discriminant Support Vector Machines, IEEE Transactions on Speech and Audio Processing (2005), Vol. 13, Issue 2, 203-210
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-09f1cc2b-7879-4831-aa6e-07bc3b9422a5
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.