Introducing i-vectors for joint anti-spoofing and speaker verification

Any biometric recognizer is vulnerable to direct spoofing attacks and automatic speaker verification (ASV) is no exception; replay, synthesis and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks. Most existing countermeasures use full knowledge of a particular VC system to detect spoofing. We study a potentially more universal approach involving generative modeling perspective. Specifically, we adopt standard ivector representation and probabilistic linear discriminant analysis (PLDA) back-end for joint operation of spoofing attack detector and ASV system. As a proof of concept, we study a vocoder-mismatched ASV and VC attack detection approach on the NIST 2006 speaker recognition evaluation corpus. We report stand-alone accuracy of both the ASV and countermeasure systems as well as their combination using score fusion and joint approach. The method holds promise. Index Terms: speaker recognition, spoofing, voice conversion attack, i-vector, joint verification and anti-spoofing

[1]  Sébastien Marcel,et al.  Anti-spoofing in Action: Joint Operation with a Verification System , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Nicholas W. D. Evans,et al.  A one-class classification approach to generalised speaker verification spoofing countermeasures using local binary patterns , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

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

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

[5]  Driss Matrouf,et al.  Effect of Speech Transformation on Impostor Acceptance , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[6]  David A. van Leeuwen,et al.  An Introduction to Application-Independent Evaluation of Speaker Recognition Systems , 2007, Speaker Classification.

[7]  Yannis Stylianou,et al.  Voice Transformation: A survey , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Sébastien Marcel,et al.  Spear: An open source toolbox for speaker recognition based on Bob , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Ibon Saratxaga,et al.  Evaluation of Speaker Verification Security and Detection of HMM-Based Synthetic Speech , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Christian Müller.  Speaker Classification II, Selected Projects , 2007, Speaker Classification.

[11]  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.

[12]  Tomoki Toda,et al.  Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  James H. Elder,et al.  Probabilistic Linear Discriminant Analysis for Inferences About Identity , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Tomi Kinnunen,et al.  Spoofing and countermeasures for automatic speaker verification , 2013, INTERSPEECH.

[15]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[16]  Thomas P. Minka,et al.  Algorithms for maximum-likelihood logistic regression , 2003 .

[17]  Pascal Druyts,et al.  Applying Logistic Regression to the Fusion of the NIST'99 1-Speaker Submissions , 2000, Digit. Signal Process..

[18]  Haizhou Li,et al.  Synthetic speech detection using temporal modulation feature , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Sébastien Marcel,et al.  Bob: a free signal processing and machine learning toolbox for researchers , 2012, ACM Multimedia.

[21]  Daniel Garcia-Romero,et al.  Analysis of i-vector Length Normalization in Speaker Recognition Systems , 2011, INTERSPEECH.

[22]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[23]  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..

[24]  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).

[25]  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).

[26]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[27]  Hagai Aronowitz,et al.  Voice transformation-based spoofing of text-dependent speaker verification systems , 2013, INTERSPEECH.

[28]  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).

[29]  David A. van Leeuwen,et al.  Fusion of Heterogeneous Speaker Recognition Systems in the STBU Submission for the NIST Speaker Recognition Evaluation 2006 , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

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

[31]  Nicholas W. D. Evans,et al.  Spoofing countermeasures to protect automatic speaker verification from voice conversion , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[32]  MarcelSebastien,et al.  A Scalable Formulation of Probabilistic Linear Discriminant Analysis , 2013 .

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