Effectiveness of energy separation-based instantaneous frequency estimation for cochlear cepstral features for synthetic and voice-converted spoofed speech detection

In this article, we propose Cochlear Filter Cepstral Coefficient-Instantaneous Frequency feature set using Energy Separation Algorithm (CFCCIF-ESA) feature set to detect the speech synthesis (SS) and voice conversion (VC)-based spoofing attacks. The SS- and VC-based spoof generation techniques predominantly uses the magnitude spectrum information, neglecting the phase information. Hence, SS and VC generated speech signal possess the distorted phase in time or frequency-domain. In this work, we exploit this anomaly in phase to efficiently detect the spoofing attack. Here, instantaneous frequency (IF) is utilized to represent the phase information as IF is nothing but the derivative of unwrapped instantaneous (analytic) phase. The experiments are performed on ASVSpoof-2015 challenge dataset, which is specifically designed to do Spoof Speech Detection (SSD) task for SS and VC. In ASVSpoof-2015 challenge during INTERSPEECH 2015, SSD system designed using Cochlear Filter Cepstral Coefficient-Instantaneous Frequency (CFCCIF) feature set was the relatively best performing system. The CFCCIF feature set composed of the information obtained from the magnitude envelope derived using cochlear filterbank and instantaneous frequency (IF) which is derived from Hilbert transform-based approach. However, Hilbert transform-based estimation requires a speech segment of 10–30 ms and thus, it limits time resolution of IF estimation and hence, defeats the key objective of IF estimation to be able to fit the frequency of a sinusoid (corresponding to a monocomponent signal) locally and almost instantaneously. Energy Separation Algorithm (ESA) is known to accurately estimate the modulation patterns due to their relatively low computational complexity, high time resolution, and instantaneously adapting nature. To that effect, we exploit the ESA instead of Hilbert transform to estimate the IFs of the subband filtered signal using cochlear filterbank. The significant improvement in performance, in particular, relative reduction of 51.21% and 46.87% in EER is observed on development and evaluation subsets, respectively, for CFCCIF-ESA feature set over its CFCCIF counterpart, using Gaussian Mixture Model (GMM)-based classifier. This improvement in the performance indicates that the IFs estimated using ESA-based approach are able to efficiently capture the artefacts produced in the instantaneous phase by the SS- and VC-based spoof signals. Furthermore, experiments are also performed with convolutional neural network (CNN) classifier, which further enhances the performance.