Cancelable speaker identification based on cepstral coefficients and comb filters

This paper presents a proposed approach for cancelable biometric recognition related to speaker identification. Both comb and inverse comb filters are used to distort speech signals intentionally prior to and after feature extraction. The objective of this approach is to generate protected templates of speech signals representing speakers without subjecting the original speech signals or features to violations of attackers. Both comb and inverse comb filters are used for this purpose. In addition, a satisfactory performance represented in the speaker identification rate is retained. In case the database of speaker features is compromised, it is possible to change the comb or inverse comb filter orders to generate new features for the same speakers. Simulation results reveal the possibility to identify speakers from their deteriorated speech signals, which proves the robustness of the proposed cancelable speaker identification system. The reason of choosing the comb filter is that it is a multi-band filter with multiple nulls in its frequency response. Hence, its inversion is difficult due to the nulling effect. This characteristic can induce non-invertible distortion in speech signals.