Processing linear prediction residual signal to counter replay attacks

Abstract

Replay attack is a method of using targets pre-recorded speech samples for acquiring unauthorized access to the automatic speaker verification (SV) system. This work investigates the usefulness of processing linear prediction (LP) residual signal to counter replay attacks. In detecting replay signals the major clues lie in tracing the record and playback devices characteristics that dominantly reflect at low frequency regions due to loud speaker, and at high frequency regions due to two-stage A/D conversions. Unlike speech, the spectral pattern of the impulse-like linear prediction (LP) residual signal is spread across entire frequency range, and so conjectured to be more useful. Based on the distribution nature of the mel-scale that tightly spaced in low frequency regions and reverse in inverse mel-scale, residual mel-frequency cepstral coefficients (RMFCC) and residual inverse mel-frequency cepstral coefficients (RIMFCC) features are used as the representative features. The effectiveness of these features is demonstrated on ASVspoof2017 database. The initial study is made on deciding the suitable prediction order. In terms of equal error rate (EER), RMFCC features provide the best performance of 14.57% from 20th order LP analysis and RIMFCC of 15.35% from 10th order LP analysis. These results show that residual signals effectively capture the low frequency distortions with higher order LP analysis and vice versa. The higher performance in case of RMFCC features may due to the significant distortion from loud speakers. The fusion of RMFCC and RIMFCC features further improves the performance to 10.14%, that is comparatively better than the state-of-the-art spectral centroid magnitude coefficients (SCMC) feature performance of 11.49%. Finally, the fusion of RMFCC and RIMFCC features together with SCMC provides 9.54%. These outcomes demonstrate the usefulness of processing LP residual signals to counter replay attacks.