Toward a Universal Synthetic Speech Spoofing Detection Using Phase Information PROJECT TITLE :Toward a Universal Synthetic Speech Spoofing Detection Using Phase InformationABSTRACT:In the sector of speaker verification (SV) it is nowadays possible and comparatively easy to make a artificial voice to deceive a speech driven biometric access system. This paper presents a artificial speech detector that can be connected at the front-finish or at the back-finish of a commonplace SV system, and that will shield it from spoofing attacks coming from state-of-the-art statistical Text to Speech (TTS) systems. The system described is a Gaussian Mixture Model (GMM) based binary classifier that uses natural and copy-synthesized signals obtained from the Wall Street Journal database to train the system models. Three different state-of-the-art vocoders are chosen and modeled using two sets of acoustic parameters: 1) relative section shift and 2) canonical Mel Frequency Cepstral Coefficients (MFCC) parameters, as baseline. The vocoder dependency of the system and multivocoder modeling options are completely studied. Additional section-aware vocoders are tested. Several experiments are carried out, showing that the phase-primarily based parameters perform better and are able to deal with new unknown attacks. The final evaluations, testing synthetic TTS signals obtained from the Blizzard challenge, validate our proposal. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Depth Sensation Enhancement for Multiple Virtual View Rendering Deep Representations for Iris, Face, and Fingerprint Spoofing Detection