Cochlear Filter and Instantaneous Frequency Based Features for Spoofed Speech Detection

Vulnerability of voice biometrics systems to spoofing attacks by synthetic speech (SS) and voice converted (VC) speech has arose the need of standalone spoofed speech detection (SSD) systems. This paper is an extension of our previously proposed features (used in relatively best performing SSD system) at the first ASVspoof 2015 challenge held at INTERSPEECH 2015. For the challenge, the authors proposed novel features based on cochlear filter cepstral coefficients (CFCC) and instantaneous frequency (IF), i.e., CFCCIF. The basic motivation behind this is that human ear processes speech in subbands. The envelope of each subband and its IF is important for perception of speech. In addition, the transient information also adds to the perceptual information that is captured. We observed that subband energy variations across CFCCIF when estimated by symmetric difference (CFCCIFS) gave better discriminative properties than CFCCIF. The features are extracted at frame level and the Gaussian mixture model based classification system was used. Experiments were conducted on ASVspoof 2015 challenge database with MFCC, CFCC, CFCCIF, and CFCCIFS features. On the evaluation dataset, after score-level fusion with MFCC, the CFCCIFS features gave an overall equal error rate (EER) of 1.45% as compared to 1.87% and 1.61% with CFCCIF and CFCC, respectively. In addition to detecting the known and unknown attacks, intensive experiments have been conducted to study the effectiveness of the features under the condition that either only SS or only VC speech is available for training. It was observed that when only VC speech is used in training, both VC, as well as SS, can be detected. However, when only SS is used in training, VC speech was not detected. In general, amongst vocoder-based spoofs, it was observed that VC speech is relatively difficult to detect than SS by the SSD system. However, vocoder-independent SS was toughest with highest EER (i.e., > 10%).

[1]  Longbiao Wang,et al.  Relative phase information for detecting human speech and spoofed speech , 2015, INTERSPEECH.

[2]  Keiichi Tokuda,et al.  A robust speaker verification system against imposture using an HMM-based speech synthesis system , 2001, INTERSPEECH.

[3]  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).

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

[5]  Paul Taylor,et al.  Festival Speech Synthesis System , 1998 .

[6]  Heiga Zen,et al.  Statistical Parametric Speech Synthesis , 2007, IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

[8]  Powen Ru,et al.  Multiresolution spectrotemporal analysis of complex sounds. , 2005, The Journal of the Acoustical Society of America.

[9]  Haizhou Li,et al.  Spoofing and countermeasures for speaker verification: A survey , 2015, Speech Commun..

[10]  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).

[11]  Yi Liu,et al.  Simultaneous utilization of spectral magnitude and phase information to extract supervectors for speaker verification anti-spoofing , 2015, INTERSPEECH.

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

[13]  Haizhou Li,et al.  Spoofing speech detection using high dimensional magnitude and phase features: the NTU approach for ASVspoof 2015 challenge , 2015, INTERSPEECH.

[14]  Jun Guo,et al.  Effect of multi-condition training and speech enhancement methods on spoofing detection , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).

[15]  Tomi Kinnunen,et al.  A comparison of features for synthetic speech detection , 2015, INTERSPEECH.

[16]  Jr. J.P. Campbell,et al.  Speaker recognition: a tutorial , 1997, Proc. IEEE.

[17]  H. Zen,et al.  An HMM-based speech synthesis system applied to English , 2002, Proceedings of 2002 IEEE Workshop on Speech Synthesis, 2002..

[18]  Aleksandr Sizov,et al.  Joint Speaker Verification and Antispoofing in the $i$ -Vector Space , 2015, IEEE Transactions on Information Forensics and Security.

[19]  Hemant A. Patil,et al.  Combining evidences from mel cepstral, cochlear filter cepstral and instantaneous frequency features for detection of natural vs. spoofed speech , 2015, INTERSPEECH.

[20]  Hemant A. Patil,et al.  Novel Nonlinear Prediction Based Features for Spoofed Speech Detection , 2016, INTERSPEECH.

[21]  Thomas Quatieri,et al.  Discrete-Time Speech Signal Processing: Principles and Practice , 2001 .

[22]  Jon Sánchez,et al.  Toward a Universal Synthetic Speech Spoofing Detection Using Phase Information , 2015, IEEE Transactions on Information Forensics and Security.

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

[24]  SaravananV,et al.  Anti-Spoofing for Text Independent Speaker Verification , 2017 .

[25]  Eric Moulines,et al.  Continuous probabilistic transform for voice conversion , 1998, IEEE Trans. Speech Audio Process..

[26]  A.E. Rosenberg,et al.  Automatic speaker verification: A review , 1976, Proceedings of the IEEE.

[27]  Hemant A. Patil,et al.  Novel Subband Autoencoder Features for Detection of Spoofed Speech , 2016, INTERSPEECH.

[28]  John H. L. Hansen,et al.  An auditory-based distortion measure with application to concatenative speech synthesis , 1998, IEEE Trans. Speech Audio Process..

[29]  Qi Li,et al.  An Auditory-Based Feature Extraction Algorithm for Robust Speaker Identification Under Mismatched Conditions , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[30]  Qi Li,et al.  An auditory-based transfrom for audio signal processing , 2009, 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

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

[32]  Hideki Kawahara,et al.  Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds , 1999, Speech Commun..

[33]  Haizhou Li,et al.  Spoofing detection from a feature representation perspective , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[34]  Alvin F. Martin,et al.  The DET curve in assessment of detection task performance , 1997, EUROSPEECH.

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

[36]  Ming Li,et al.  The SYSU system for the interspeech 2015 automatic speaker verification spoofing and countermeasures challenge , 2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[37]  Keiichi Tokuda,et al.  Imposture using synthetic speech against speaker verification based on spectrum and pitch , 2000, INTERSPEECH.

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

[39]  Nicholas W. D. Evans,et al.  A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients , 2016, Odyssey.

[40]  Keiichi Tokuda,et al.  An adaptive algorithm for mel-cepstral analysis of speech , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[41]  Li-Rong Dai,et al.  Speaker verification against synthetic speech , 2010, 2010 7th International Symposium on Chinese Spoken Language Processing.

[42]  Hemant A. Patil,et al.  Effectiveness of fundamental frequency (F0) and strength of excitation (SOE) for spoofed speech detection , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[43]  Bo Chen,et al.  Robust deep feature for spoofing detection - the SJTU system for ASVspoof 2015 challenge , 2015, INTERSPEECH.

[44]  Jon Sánchez,et al.  Synthetic speech detection using phase information , 2016, Speech Commun..

[45]  Artur Janicki.  Spoofing countermeasure based on analysis of linear prediction error , 2015, INTERSPEECH.

[46]  Galina Lavrentyeva,et al.  STC anti-spoofing systems for the ASVspoof 2015 challenge , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[48]  Aleksandr Sizov,et al.  ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge , 2015, INTERSPEECH.

[49]  Tomoki Toda,et al.  SAS: A speaker verification spoofing database containing diverse attacks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[50]  Themos Stafylakis,et al.  Development of CRIM system for the automatic speaker verification spoofing and countermeasures challenge 2015 , 2015, INTERSPEECH.

[51]  Eduardo Lleida,et al.  Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge , 2015, INTERSPEECH.

[52]  S Shamma,et al.  The case of the missing pitch templates: how harmonic templates emerge in the early auditory system. , 2000, The Journal of the Acoustical Society of America.

[53]  Volker Hohmann,et al.  Acoustic features for speech recognition based on Gammatone filterbank and instantaneous frequency , 2011, Speech Commun..

[54]  Zachary M. Smith,et al.  Chimaeric sounds reveal dichotomies in auditory perception , 2002, Nature.

[55]  Jon Sánchez,et al.  The AHOLAB RPS SSD spoofing challenge 2015 submission , 2015, INTERSPEECH.

[56]  Namrata Singh,et al.  NOVEL COCHLEAR FILTER BASED CEPSTRAL COEFFICIENTS FOR CLASSIFICATION OF UNVOICED FRICATIVES , 2014 .

[57]  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.

[58]  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.

[59]  Takao Kobayashi,et al.  Analysis of Speaker Adaptation Algorithms for HMM-Based Speech Synthesis and a Constrained SMAPLR Adaptation Algorithm , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[60]  Kuldip K. Paliwal,et al.  On the usefulness of STFT phase spectrum in human listening tests , 2005, Speech Commun..

[61]  Florin Curelaru,et al.  Front-End Factor Analysis For Speaker Verification , 2018, 2018 International Conference on Communications (COMM).