Spoofing countermeasures to protect automatic speaker verification from voice conversion

This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure exploits the common shift applied to the spectral slope of consecutive speech frames involved in the mapping of a spoofer's voice signal towards a statistical model of a given target. While the countermeasure exploits prior knowledge of the attack in an admittedly unrealistic sense, it is shown to detect almost all spoofed signals which otherwise provoke significant increases in false acceptance. The work also discusses the need for formal evaluations to develop new countermeasures which are less reliant on prior knowledge.

[1]  Nicholas W. D. Evans,et al.  ALIZE/spkdet: a state-of-the-art open source software for speaker recognition , 2008, Odyssey.

[2]  Driss Matrouf,et al.  Artificial impostor voice transformation effects on false acceptance rates , 2007, INTERSPEECH.

[3]  John H. L. Hansen,et al.  An experimental study of speaker verification sensitivity to computer voice-altered imposters , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[4]  Matti Pietikäinen,et al.  Competition on counter measures to 2-D facial spoofing attacks , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[5]  Keiichi Tokuda,et al.  On the security of HMM-based speaker verification systems against imposture using synthetic speech , 1999, EUROSPEECH.

[6]  Driss Matrouf,et al.  A straightforward and efficient implementation of the factor analysis model for speaker verification , 2007, INTERSPEECH.

[7]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[8]  Haizhou Li,et al.  Detecting Converted Speech and Natural Speech for anti-Spoofing Attack in Speaker Recognition , 2012, INTERSPEECH.

[9]  A. Hadid,et al.  TABULA RASA Trusted Biometrics under Spoofing Attacks , 2011 .

[10]  David Talkin,et al.  A Robust Algorithm for Pitch Tracking ( RAPT ) , 2005 .

[11]  Mireia Farrús,et al.  How vulnerable are prosodic features to professional imitators? , 2008, Odyssey.

[12]  Ibon Saratxaga,et al.  Detection of synthetic speech for the problem of imposture , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Chng Eng Siong,et al.  Vulnerability of speaker verification systems against voice conversion spoofing attacks: The case of telephone speech , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Mats Blomberg,et al.  Vulnerability in speaker verification - a study of technical impostor techniques , 1999, EUROSPEECH.

[15]  Nicholas W. D. Evans,et al.  On the vulnerability of automatic speaker recognition to spoofing attacks with artificial signals , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[16]  Akio Ogihara,et al.  Discrimination Method of Synthetic Speech Using Pitch Frequency against Synthetic Speech Falsification , 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[17]  Driss Matrouf,et al.  Transfer Function-Based Voice Transformation for Speaker Recognition , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[18]  Federico Alegre,et al.  Spoofing countermeasures for the protection of automatic speaker recognition from attacks with artificial signals , 2012 .

[19]  Driss Matrouf,et al.  State-of-the-Art Performance in Text-Independent Speaker Verification Through Open-Source Software , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[20]  Daniel Elenius,et al.  Speaker verification scores and acoustic analysis of a professional impersonator , 2004 .

[21]  Douglas E. Sturim,et al.  SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[22]  Haizhou Li,et al.  A study on spoofing attack in state-of-the-art speaker verification: the telephone speech case , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[23]  Matti Pietikäinen,et al.  Face spoofing detection from single images using texture and local shape analysis , 2012, IET Biom..

[24]  Nicholas W. D. Evans,et al.  Spoofing countermeasures for the protection of automatic speaker recognition systems against attacks with artificial signals , 2012, INTERSPEECH.

[25]  Junichi Yamagishi,et al.  Evaluation of the Vulnerability of Speaker Verification to Synthetic Speech , 2010, Odyssey.

[26]  Junichi Yamagishi,et al.  Synthetic Speech Discrimination using Pitch Pattern Statistics Derived from Image Analysis , 2012, INTERSPEECH.

[27]  Gérard Chollet,et al.  Voice forgery using ALISP: indexation in a client memory , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..