Speaker Recognition System Based on GMM Multivariate Probability Distributions built-in a Digital Watermarking Token

• Autorzy: Lenarczyk P. , Piotrowski Z.

Warianty tytułu

PL

System rozpoznawania mówcy na podstawie wielowariancyjnych rozkładów prawdopodobieństwa zaimplementowany w tokenie znaku wodnego

• Języki publikacji: EN

Abstrakty

EN

The article describes a speaker recognition system based on continuous speech using GMM multivariate probability distributions. A theoretical model of the system including the extraction of distinctive features and statistical modeling is described. The efficiency of the system implemented in the Linux operating system was determined. The system is designed to support the functionality of the Personal Trusted Terminal PTT in order to uniquely identify a subscriber using the device.

PL

Przedstawiony poniżej artykuł opisuje system rozpoznawania mówcy na podstawie mowy ciągłej, wykorzystując wielowariancyjne rozkłady prawdopodobieństwa GMM. Opisane zostały procesy ekstrakcji cech dystynktywnych głosu oraz tworzenia modeli statystycznych. Algorytm został zaimplementowany w systemie Linux w celu poprawy funkcjonalności identyfikacji użytkownika Zaufanego Osobistego Terminalu PTT.

Słowa kluczowe

EN

GMM; speaker recognition; PTT; biometrics

PL

GMM; rozpoznawanie mówcy; PTT; biometria

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 2a
• Strony: 59--63
• Opis fizyczny: Bibliogr. 17 poz., schem., wykr.

Twórcy

autor

Lenarczyk P.

• Military University of Technology, Faculty of Electronics, Warsaw, Poland, Tel. [phone], Fax. [phone]

autor

Piotrowski Z.

• Military University of Technology Faculty of Electronics, Warsaw, Poland, Tel. [phone], Fax. [phone]

Bibliografia

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• [3] Davis, S., Merlmestein, P., Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences, IEEE Trans. on ASSP (1980), 357-366
• [4] Neiberg D., Text Independent Speaker Verication Using Adapted Gaussian Mixture Models CTT (2001)
• [5] Joseph P., Campbell, Jr., Speaker recognition, PII: S 0018- 9219(97)06947-8
• [6] Reynolds D.A., Rose R.C., Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE Trans. Speech Audio Process, 3 (1995), 72–83
• [7] Reynolds D.A., Speaker identification and verification using Gaussian mixture speaker models, Speech Commun. 17 (1995), 91–108
• [8] Rabiner L., Juang B.H., Fundamentals of Speech Recognition, Prentice-Hall (1993)
• [9] Markel J., Oshika B., Gray Jr. A., Long-term feature averaging for speaker reognition. ZEEE Transactions on Acoustics, Speech, and Signal Processing, August (1977),54-61
• [10] McLachlan G., Peel D., Finite Mixture Models. Hoboken, NJ: John Wiley & Sons, Inc., (2000)
• [11] Reynolds D.A., Automatic speaker recognition using Gaussian mixture speaker models, Lincoln Lab. J, 8 (1996), 173–192
• [12] Reynolds D.A., Comparison of Background Normalization Methods for Text-Independent Speaker Verification, Proceedings of Eurospeech, (1997), 963–966
• [13] Reynolds D.A., Quatieri T.F., Dunn R.B, Speaker Verification Using Adapted Gaussian Mixture Models, Digital Signal Processing, (2000)
• [14] Jiang H., Confidence Measures For Speech Recognition, Survey A., Speech Communication, Volume 45 No. 4 (2005), 455–470
• [15] Dempster A.P., Laird N.M., Rubin D.B., Maximum-Likelihood From Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Ser. B., 39 (1977)
• [16] Bilmes, J., Gentle A., Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, International Computer Science Institute, (1998)
• [17] Jiang, H., Confidence Measures For Speech Recognition, Survey A., Speech Communication, Volume 45 No. 4(2005), 455–470