Table 5 shows the performance of CQCC independently for each of the dif-ferent spoofing attacks grouped into known and unknown attacks. Results are presented here for only the 19-th order feature set with A coefficients only. The
average EER of this system is 0.36%. Also illustrated for comparison is the per-formance of the four systems described in Section 2.22.
Focusing first on known attacks, all four systems deliver excellent error rates of below 0.41%. The proposed CQCC features are third in the ranking according to the average error rate, with an EER of 0.067%. Voice conversion attacks S2 and S5 seem to be the most difficult to detect. Speech synthesis attacks S3 and S4, however, are perfectly detected by all systems.
It is for unknown attacks where the difference between systems is greatest.
Whereas attacks S6, S7 and S9 are detected reliably by all systems, there is con-siderable variation for attacks S8 and S10. In particular, the performance for attack S10, the only unit-selection-based speech synthesis algorithm, varies considerably;
past results range from 8.2% to 26.1%. Being so much higher than the error rates for other attacks, the average performance for unknown attacks is dominated by the performance for S10. Average error rates for past work and unknown attacks range from 1.7% to 5.2%.
CQCC features compare favourably. While the performance for S6, S7 and S9 is not as good as that of other systems, error rates are still low and below 0.1%.
While the error rate for S8 of 1.8% is considerably higher than for other systems, it is significantly better than all other systems for attack S10. Here is the error is reduced to 1.1%. This corresponds to a relative improvement of 86% with regard to the next best performing system for S10. The average performance of CQCC features for unknown attacks is 0.7%. This corresponds to a relative improvement of 61% over the next best system.
The average performance across all 10 spoofing attacks is illustrated in the fi-nal column of Table 5. The average error rate of 0.360% is significantly better than those reported in previous work. The picture of generalisation is thus not straight-forward. While performance for unknown attacks is worse than it is for known at-tacks, CQCC features nonetheless deliver the most consistent performance across the 10 different spoofing attacks in the ASVspoof database. Even if it must be acknowledge that the work reported in this paper was conducted post-evaluation, to the authors’ best knowledge, CQCC features give the best spoofing detection performance reported to date.
7 Conclusions
This paper introduces a new feature for the automatic detection of spoofing attacks which can threaten the reliability of automatic speaker verification. The new feature is based upon the constant Q transform and is combined with tradi-tional cepstral analysis. Termed constant Q cepstral coffficients (CFCCs), the new features provide a variable-resolution, time-frequency representation of the
spec-2Thanks to Md. Shahid Ullah and Tomi Kinnunen from the University of Eastern Findland for kindly providing individual results on all spoofing attacks.
Table5:Performance(EER)ofthebestperformingsystem,includingindividualresultsoneachspoofingattack,averageknownand unknownattacks,andtotalaverage.Standarddeviationisprovidedasameasureofgeneralisationforunknownattacks.Resultsbythe systemsreviewedinSection2areincludedforcomparison. KnownAttacksUnknownAttacksAll SystemS1S2S3S4S5Avg.S6S7S8S9S10Avg.Avg. CFCC-IF0.1010.8630.0000.0001.0750.4080.8460.2420.1420.3468.4902.0131.211 i-vector0.0040.0220.0000.0000.0130.0080.0190.0000.0150.00419.573.9221.965 M&Pfeat.0.0000.0000.0000.0000.0100.0020.0100.0000.0000.00026.105.2222.612 LFCC-DA0.0270.4080.0000.0000.1140.1100.1490.0110.0740.0278.1851.6700.890 CQCC-A0.0050.1490.0000.0000.1790.0670.1520.0711.8290.0741.1360.6530.360
trum which captures detailed characteristics which are missed by more classical approaches to feature extraction.
These characteristics are shown to be informative for spoofing detection. When coupled with a simple Gaussian mixture model-based classifier and assessed on a standard database, CQCC features outperform all existing approaches to spoofing detection. In addition, while there is still a marked discrepancy between perfor-mance for known and unknown spoofing attacks, CQCC results correspond to a relative improvements of 61% over the previously best performing system. Fu-ture work should consider the application of CQCCs for more generalised counter-measures such as a 1-class classifier. The application of CQCCs in other speaker recognition and related problems is another obvious direction.
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