Effectiveness of fundamental frequency (F0) and strength of excitation (SOE) for spoofed speech detection

Abstract

Current countermeasures used in spoof detectors (for speech synthesis (SS) and voice conversion (VC)) are generally phase-based (as vocoders in SS and VC systems lack phase-information). These approaches may possibly fail for non-vocoder or unit-selection-based spoofs. In this work, we explore excitation source-based features, i.e., fundamental frequency (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) contour and strength of excitation (SoE) at the glottis as discriminative features using GMM-based classification system. We use F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and SoE1 estimated from speech signal through zero frequency (ZF) filtering method. Further, SoE2 is estimated from negative peaks of derivative of glottal flow waveform (dGFW) at glottal closure instants (GCIs). On the evaluation set of ASVspoof 2015 challenge database, the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and SoEs features along with its dynamic variations achieve an Equal Error Rate (EER) of 12.41%. The source features are fused at score-level with MFCC and recently proposed cochlear filter cepstral coefficients and instantaneous frequency (CFCCIF) features. On fusion with MFCC (CFCCIF), the EER decreases from 4.08% to 3.26% (2.07% to 1.72%). The decrease in EER was evident on both known and unknown vocoder-based attacks. When MFCC, CFCCIF and source features are combined, the EER further decreased to 1.61%. Thus, source features captures complementary information than MFCC and CFCCIF used alone.